{ "schema": "omni-reason-full-public-manifest-v1", "snapshot_time": "2026-10-18T05:16:26.993489+00:00", "generated_at": "2026-10-18T05:30:59.428130+00:00", "source": { "quality_ledger": "2026-10-18_full170-quality-accounting-v21.json", "quality_ledger_sha256": "e058274403bc770eac32a01646c76b0db6ae2edc1c909bb8f75e559ef9f74631", "accounting": "2026-10-13_full170-production-accounting-20261018T050000.581535Z.json", "accounting_sha256": "961d62ee552cd6d0d499c8c4a784d76ae166f33d2eee022721978f0a47110691" }, "scope": { "tasks": 170, "models": 7, "samples_per_task": 5, "identities": 5950, "excluded_tasks": [ "Task_379_sar_guided_multitemporal_satellite_cloud_removal" ] }, "counts": { "quality_passed": 4781, "public_playable": 4777, "quality_passed_not_public": 4, "automated_visual_integrity_passed": 4751, "reviewed_visual_integrity_passed": 14, "recovered_visual_integrity_passed": 16, "confirmed_invalid": 487, "needs_review": 35, "not_checked": 4, "not_file_completed": 643 }, "quality_contract": { "meaning": "Visual and protocol integrity only", "semantic_task_correctness_certified": false, "playable_requires": "quality pass plus selected S3 video and sidecar with exact SHA-256 and immutable VersionId verification" }, "tasks": [ { "id": "Task_24_reference_guided_bimanual_robot_exo_to_head_ego_translation", "name": "Reference-Guided Bimanual Robot Exo-to-Head-Ego Translation", "modality": "rv2v", "prompt": "Using reference_image1 as the initial robot-head-camera view, translate the complete synchronized external-front-camera video into the robot-head-camera view of the same Airbot MMK2 episode. Preserve both arms and dexterous hands, object states, contact events, action order, scene geometry, and frame-level timing." }, { "id": "Task_251_real_robot_cross_view_translation", "name": "Real Robot Cross-View Translation", "modality": "rv2v", "prompt": "Using reference_image1 as the initial wrist-camera view, translate the synchronized external-camera video into the wrist-camera view of the same robot episode. Preserve the robot action, scene geometry, and frame-level timing." }, { "id": "Task_256_lane_topology_annotation", "name": "lane_topology_annotation", "modality": "rv2v", "prompt": "Identify the lane structure of the road in the driving video. In every frame, draw each lane centerline in a consistent color, and mark lane connectivity with short arrows from the end of each lane to the start of its successor lane. Keep the same color for the same lane across frames and keep everything else in the video unchanged." }, { "id": "Task_261_masked_visual_action_conditioned_video_generation", "name": "masked_visual_action_conditioned_video_generation", "modality": "rv2v", "prompt": "Using the RGB first-frame Reference as the scene appearance and the gray-background masked Input as robot-motion guidance, reconstruct the complete RGB video while performing this action: pnp-table-to-sink-chips. Preserve the camera view, timing, background, non-robot objects, and visible interactions. Keep the robot motion aligned with the Input; do not add unrelated motion or scene changes." }, { "id": "Task_277_event_guided_rgb_video_continuation", "name": "event_guided_rgb_video_continuation", "modality": "rv2v", "prompt": "Using the preceding RGB reference clip to preserve appearance, color, and the target-camera viewpoint, generate the next 81 frames from the same RGB camera. Use the temporally synchronized adjacent-camera event visualization to guide motion and brightness changes during the target interval. Preserve continuity from the reference; do not assume that the Event and RGB views are pixel-aligned." }, { "id": "Task_283_robot_skeleton_action_visualization", "name": "robot_skeleton_action_visualization", "modality": "rv2v", "prompt": "Overlay the synchronized colored robot skeleton from the Reference video onto the corresponding frames of the Input video. Preserve the Input scene, motion, timing, and all pixels outside the Reference video's colored link lines and joint markers." }, { "id": "Task_284_robot_EEF_action_visualization", "name": "robot_EEF_action_visualization", "modality": "rv2v", "prompt": "Overlay the synchronized end-effector coordinate-frame visualization from the Reference video onto the corresponding frames of the Input video. Preserve the Input scene, motion, timing, and all pixels outside the Reference video's red, green, and blue coordinate axes and dark origin mark." }, { "id": "Task_303_industrial_egocentric_temporal_gap_repair_with_exocentric_video_reference", "name": "industrial_egocentric_occlusion_repair_with_exocentric_reference", "modality": "rv2v", "prompt": "Using the synchronized third-person reference video, restore the missing interval in this first-person industrial procedure. Preserve the first-person viewpoint, hand-object interaction, object state, and temporal order. Procedure: Turns on the oscilloscope via the tower switch." }, { "id": "Task_320_urdf_conditioned_egocentric_human_to_robot_video_editing", "name": "urdf_conditioned_egocentric_human_to_robot_video_editing", "modality": "rv2v", "prompt": "Replace the visible human hand with the specified shadow robot embodiment while preserving object state, interaction, background, action semantics, camera motion, timeline, frame count, and frame rate." }, { "id": "Task_322_reference_conditioned_physical_point_tracking", "name": "reference_conditioned_physical_point_tracking", "modality": "rv2v", "prompt": "The Reference image shows the desired first frame of the Target video and is temporally aligned with frame 0 of the Input video. The center of the red marker identifies one specific material point on an object or scene surface. Track the projected location of that exact same surface point throughout the Input video. Do not keep the marker at a fixed screen coordinate, move it to the object center, or substitute a nearby point. Show no marker while the point is occluded or outside the frame, and resume tracking it when it becomes visible again. Do not draw a trajectory, and preserve the original video everywhere else." }, { "id": "Task_339_reference_guided_robot_cycle_timing_normalization", "name": "reference_guided_robot_cycle_timing_normalization", "modality": "rv2v", "prompt": "Use the Reference video only as a guide to the normal relative timing of this robot task: Grip and lift the black laptop with reflective screen's lid.\n\nThe Input has this local timing defect: action phase 5 runs at roughly double speed, and phase 4 contains a clearly visible 1.0-second freeze; phase 1 keeps total duration unchanged\n\nNormalize the task's action phases to the Reference rhythm while preserving the Input robot, camera, scene, objects, action order, and successful result. Keep Input and Target equal in frame count, FPS, resolution, and total duration. Do not copy Reference appearance, reverse or skip actions, add new motion, or create visible jumps or interpolation artifacts. Return only the normalized Target video." }, { "id": "Task_375_prompt_conditioned_robot_subgoal_progress_visualization", "name": "Prompt Conditioned Robot Subgoal Progress Visualization", "modality": "rv2v", "prompt": "Use the VIMA multimodal prompt shown in the Reference image to interpret this robot-task rollout. Add an English task-progress annotation only to the neutral status band below the robot scene. Preserve every captured Input frame in its exact order, keep the robot scene unchanged, show exactly one current semantic subgoal at a time, advance progress only when simulator goal state advances, and keep repeated attempts on the same stage. Do not cover, crop, loop, reorder, drop, or interpolate any Input frame." }, { "id": "Task_391_language_grounded_multi_object_grasp_video_generation", "name": "language_grounded_multi_object_grasp_video_generation", "modality": "rv2v", "prompt": "Continue the scene by reaching for the yellow banana, grasping it, and lifting it from the table." }, { "id": "Task_394_synchronized_stereo_rgb_to_disparity_visualization", "name": "synchronized_stereo_rgb_to_disparity_visualization", "modality": "rv2v", "prompt": "Using the synchronized rectified right-camera reference video, convert the left-camera input video into a frame-aligned disparity visualization. Preserve the exact frame count, timing, resolution, and driving sequence. Use the fixed task-wide 0-192 source-pixel Turbo color scale and render invalid disparity pixels as black." }, { "id": "Task_413_external_to_eye_in_hand_video_translation", "name": "External To Eye In Hand Video Translation", "modality": "rv2v", "prompt": "Using the reference image of the first output frame, translate the input external-camera video into the synchronized eye-in-hand camera view of the same episode. Preserve the object identity, actions, timing, and frame order. Match the moving wrist-camera viewpoint throughout; do not add labels, force indicators, or other overlays." }, { "id": "Task_422_reference_guided_cross_wrist_garment_manipulation_view_translation", "name": "Reference Guided Cross Wrist Garment Manipulation View Translation", "modality": "rv2v", "prompt": "Using the synchronized top-view Reference video of this complete dual-arm garment-folding episode, translate the left-wrist RGB Input into the corresponding right-wrist RGB camera view over the same complete timeline. Preserve garment state, robot motion, contacts, frame order, full duration, and native 30 FPS timing." }, { "id": "Task_452_reference_guided_analogical_chess_puzzle_solving", "name": "Reference Guided Analogical Chess Puzzle Solving", "modality": "rv2v", "prompt": "Apply the tactical idea demonstrated in the reference game to solve the current chess puzzle. Show the complete sequence of moves from the current position, including both sides' responses." }, { "id": "Task_453_lewm_pusht_goal_conditioned_video_rollout", "name": "Lewm Pusht Goal Conditioned Video Rollout", "modality": "rv2v", "prompt": "Continue the PushT manipulation shown in the Input toward the achieved final state shown in the Reference image. Return only the future scene, ending at that referenced state." }, { "id": "Task_470_pldm_tworooms_goal_conditioned_rollout", "name": "Pldm Tworooms Goal Conditioned Rollout", "modality": "rv2v", "prompt": "Continue the Two-Rooms navigation from the history video using the single canonical shortest route: move in a straight line to the exact center of the doorway, then in a straight line to the achieved goal state shown in the reference image, at constant speed. Return only that future scene and end exactly at the referenced goal state." }, { "id": "Task_479_reference_guided_progressive_makeup_transfer", "name": "Reference Guided Progressive Makeup Transfer", "modality": "rv2v", "prompt": "Apply the reference makeup progressively: first the eye makeup, then the lip makeup, then reveal the remaining makeup and complexion result from left to right. Preserve identity and pose." }, { "id": "Task_494_robot_multiview_reproduction", "name": "Robot Multiview Reproduction", "modality": "rv2v", "prompt": "Reproduce the complete robot interaction in the input video from the fixed camera viewpoint shown in the reference image. Preserve the identities of the robot and objects, the action progression, and the temporal order. Keep the reference viewpoint fixed throughout the output video." }, { "id": "Task_530_query_cell_descendant_lineage_tracking_video", "name": "Query Cell Descendant Lineage Tracking Video", "modality": "rv2v", "prompt": "The reference image is the first frame of the input video with one cell circled in cyan. Use that circle to identify the cell to track. Return the entire input video with a cyan circular outline centered on that cell in every frame, starting from the first frame. Whenever it divides, replace the mother's outline with one outline on each daughter, then continue marking all subsequent descendants in the same way. Do not mark unrelated cells. Use the same cyan color, circle size, and outline style as in the reference image throughout. Preserve the original scene, frame order, frame count, and playback timing. Do not prepend the reference image, add text or lineage-dependent colors, or invent new biological events." }, { "id": "Task_579_rgb_with_first_frame_object_reference_to_publisher_mask_overlay", "name": "Rgb With First Frame Object Reference To Publisher Mask Overlay", "modality": "rv2v", "prompt": "Using the highlighted object in the reference image, overlay its visible region in every frame of the input video with a semi-transparent magenta mask. Preserve the original RGB scene, timing and frame order." }, { "id": "Task_698_reference_guided_video_colorization", "name": "Reference Guided Video Colorization", "modality": "rv2v", "prompt": "Colorize the entire grayscale video using the provided RGB image of its first frame as the color reference. Keep the colors of people, objects, and the scene consistent with the reference and stable over time. Preserve all motion, camera movement, structure, frame timing, and resolution. Return the complete color video." }, { "id": "S-269_temporal_rewrite", "name": "Temporal Rewrite", "modality": "tv2v", "prompt": "Recreate the same robot actions and scene from the input, but change their temporal order to: push pink block right → move slider left → turn on led → turn on lightbulb. Complete each action in that exact order. Keep the initial state, camera, objects, and all other physical behavior unchanged." }, { "id": "Task_105_video_harmonization", "name": "video_harmonization", "modality": "tv2v", "prompt": "Harmonize the foreground color and illumination with the background while preserving the scene content and motion." }, { "id": "Task_10_sports_analysis", "name": "sports analysis", "modality": "tv2v", "prompt": "Add a frame-aligned player-tracking overlay to this 120-frame top-view soccer clip. Draw a cyan bounding box around every tracked player; cyan means player extent only, not team membership. Assign white labels P001, P002, and so on in window-local canonical first-appearance order: earlier first appearances come first, and players first appearing together are ordered left-to-right, then top-to-bottom. Keep each label attached to the same player within this clip. When a player has moved more than 2 output pixels from its position five frames earlier, draw a yellow arrow from the current player center in that motion direction, capped at 28 output pixels. Draw a white bounding box around the visible ball. Do not add team colors, team classifications, INPUT/TARGET titles, or any other change. Preserve the scene, frame order, geometry, and timing." }, { "id": "Task_115_video_semantic_segmentation", "name": "video_semantic_segmentation", "modality": "tv2v", "prompt": "Perform semantic segmentation on every frame. Return a temporally aligned per-pixel VSPW label video. Use only these category colors for this sample:\nothers | RGB(0, 0, 0)\nground | RGB(191, 128, 128)\nbuilding | RGB(22, 22, 22)\nother_construction | RGB(28, 28, 28)\nbillboard_or_Bulletin_Board | RGB(41, 41, 41)\nbicycle | RGB(52, 52, 52)\nperson | RGB(61, 61, 61)\ntree | RGB(67, 67, 67)\nbag_or_package | RGB(80, 80, 80)\nPreserve frame count, order, timing, and geometry. Return no source-RGB overlay or audio." }, { "id": "Task_117_fisheye_video_rectification", "name": "fish-eye video rectification", "modality": "tv2v", "prompt": "Rectify the fisheye video into a geometrically corrected perspective video while preserving the scene content and temporal alignment." }, { "id": "Task_121_indoor_video_dehazing", "name": "indoor_video_dehazing", "modality": "tv2v", "prompt": "Remove the visible haze from this indoor video while preserving the original scene, camera motion, frame timing, framing, colors, textures, and all scene content." }, { "id": "Task_130_video_motion_deblurring", "name": "Video Deblurring", "modality": "tv2v", "prompt": "Remove motion blur from this video and restore a sharp video. Preserve the original scene content, camera trajectory, subject motion, frame order, timing, framing, resolution, colors, textures, and illumination; do not stabilize, crop, recolor, retime, add, or remove anything." }, { "id": "Task_131_robot_table_manipulation_prediction", "name": "Robot_table_manipulation_prediction", "modality": "tv2v", "prompt": "Continue the observed robot manipulation: Use the right arm to grab the smooth white can with accents, put it into the carrying basket with black plastic handle, and lift the carrying basket with black plastic handle with another arm." }, { "id": "Task_132_outdoor_video_dehazing", "name": "Outdoor Image Dehazing", "modality": "tv2v", "prompt": "Remove the haze from this outdoor video and restore the aligned clear scene while preserving camera motion, frame order, timing, framing, colors, textures, objects, and all other scene content." }, { "id": "Task_133_video_saliency_gaze_prediction", "name": "Video Saliency Prediction (DHF1K)", "modality": "tv2v", "prompt": "Predict where human visual attention focuses in this video and render the predicted attention as a semi-transparent heat map overlaid on the original frames, keeping all original content visible." }, { "id": "Task_135_low_light_video_enhancement", "name": "Low-Light Video Enhancement", "modality": "tv2v", "prompt": "Enhance this low-light video to normal exposure, recovering scene colors and details while suppressing noise and preserving the original scene content and motion." }, { "id": "Task_13_novel_view_generation", "name": "novel view generation", "modality": "tv2v", "prompt": "Generate a smooth 360-degree turntable video of the same object. Preserve its identity, geometry, color, texture, camera elevation, and camera distance across every viewpoint." }, { "id": "Task_144_video_instance_identity_tracking", "name": "Video Instance Identity Tracking", "modality": "tv2v", "prompt": "Render a frame-aligned instance mask video on a black background. Give every annotated object instance a distinct color and keep that instance's color unchanged for the complete video." }, { "id": "Task_14_ball_tracking", "name": "ball tracking", "modality": "tv2v", "prompt": "Track the tennis ball through the clip. Produce the same frame-aligned video with a red circle centered at its current or temporally inferred position while it remains in frame, including frames where it is difficult to identify or occluded. Draw no circle once no ball position exists. Preserve every other visible element, frame, and timing relationship." }, { "id": "Task_150_language_grounded_target_tracking", "name": "language_grounded_target_tracking", "modality": "tv2v", "prompt": "Track the target described as \"white horse walking with another horse in the field\" throughout the video. In every frame where it is visible, draw a tight red bounding box around it. Do not draw the box when the target is fully occluded or out of view. Keep everything else in the video unchanged." }, { "id": "Task_151_instruction_guided_video_object_addition", "name": "Instruction-Guided Video Object Addition", "modality": "tv2v", "prompt": "Add a man with short reddish-brown hair and a dark maroon long-sleeved shirt, leaning over the kitchen counter in the middle-left of the frame." }, { "id": "Task_152_instruction_guided_video_object_removal", "name": "Instruction-Guided Video Object Removal", "modality": "tv2v", "prompt": "Remove the laptop with the Apple logo." }, { "id": "Task_153_instruction_guided_video_object_replacement", "name": "Instruction-Guided Video Object Replacement", "modality": "tv2v", "prompt": "Replace the young woman with blonde hair and a light-colored top in the center with a large sunflower with a bee on it." }, { "id": "Task_154_instruction_guided_video_stylization", "name": "Instruction-Guided Video Stylization", "modality": "tv2v", "prompt": "Convert the video into a Watercolor Painting style" }, { "id": "Task_156_motion_expression_referring_segmentation", "name": "motion expression referring segmentation", "modality": "tv2v", "prompt": "Segment the object or objects described as \"The car that is currently in the process of driving.\" throughout the video. In every frame, overlay a semi-transparent red mask that exactly covers the described object(s) wherever they are visible. Do not cover any other object, and keep everything else in the video unchanged." }, { "id": "Task_157_hand_object_interaction_segmentation", "name": "hand–object interaction segmentation", "modality": "tv2v", "prompt": "Identify the hands and the object entities associated with their ongoing interactions throughout the video. In every frame, overlay a semi-transparent blue mask on the left hand, a green mask on the right hand, and a red mask on all visible regions of each object entity associated with those hand interactions. Keep everything else in the video unchanged." }, { "id": "Task_163_temporal_video_alpha_matting", "name": "temporal_video_alpha_matting", "modality": "tv2v", "prompt": "Extract a temporally stable grayscale alpha matte for the foreground subject in every frame, preserving the source frame order, timing, geometry, and fine boundary details." }, { "id": "Task_164_screen_video_moire_removal", "name": "screen_video_moire_removal", "modality": "tv2v", "prompt": "Remove spatial and temporal screen-capture moire patterns while preserving the true scene texture, edges, color, motion, framing, and timing." }, { "id": "Task_16_scene_novel_view_generation", "name": "product 360 display", "modality": "tv2v", "prompt": "Given this video of 4 ordered sparse observations from one real indoor camera traversal, generate the 63-frame dense traversal between them at 10 fps. Preserve the same scene geometry, object identity, layout, materials, and lighting; do not add, remove, or move scene content. Only the sparse RGB video is provided—no camera poses or separate reference media are available." }, { "id": "Task_171_human_skeleton_annotation", "name": "human skeleton annotation", "modality": "tv2v", "prompt": "Annotate the 2D human body skeleton throughout the video. In every frame, draw the available COCO keypoints as dots and connect them with bones, using blue for the left-side limbs, green for the right-side limbs, and yellow for the torso and head. Do not draw a keypoint when its 2D annotation is missing, invalid, low-confidence, or outside the frame, and do not draw any bone connected to an omitted keypoint. Keep everything else in the video unchanged." }, { "id": "Task_17_billiard_trajectory_prediction", "name": "billiard trajectory", "modality": "tv2v", "prompt": "Continue the billiard-ball trajectories, including cushion bounces and ball collisions." }, { "id": "Task_181_category_instance_counting", "name": "category_instance_counting", "modality": "tv2v", "prompt": "Count the \"person\" instances that appear in the video. In every frame, show a counter in the top-left corner displaying how many distinct person instances have appeared so far. When a new instance appears for the first time, increase the counter by one and briefly highlight that instance with a red outline. Do not count the same instance twice if it leaves and comes back, and keep everything else in the video unchanged." }, { "id": "Task_182_first_frame_guided_single_object_video_segmentation", "name": "First-Frame-Guided Single-Object Video Segmentation", "modality": "tv2v", "prompt": "Track the object indicated by the cyan-green overlay in the first frame. In every frame where it is visible, highlight that same object with the same cyan-green translucent overlay; when it is not visible, leave the original RGB frame unchanged." }, { "id": "Task_184_slam_loop_closure_detection", "name": "Slam Loop Closure Detection", "modality": "tv2v", "prompt": "Detect loop closure in this single, chronological camera video. Keep the original footage, frame order, geometry, and timing unchanged. If the view near the end has recognizable visual overlap with a place shown earlier in the same video, add a green border and a LOOP CLOSURE banner, plus a top-right thumbnail copied from the matched earlier frame. If the view near the end has no visually overlapping earlier place, add a red border and a NO LOOP banner. Do not split the frame or introduce a second input video." }, { "id": "Task_194_tracking_error_correction", "name": "tracking_error_correction", "modality": "tv2v", "prompt": "The yellow box overlaid on this video is an object-tracking result that contains errors: it may drift away from the target, jump to a wrong object, lag behind, change to a wrong size, or disappear while the target is still visible. The box is correct at the beginning of the video. Output the same video with the yellow box corrected so that it tightly follows the intended target in every frame, and keep everything else in the video unchanged." }, { "id": "Task_195_subject_auto_reframe", "name": "subject_auto_reframe", "modality": "tv2v", "prompt": "The cyan square in the input video marks the exact region to use for the target. Output the visual content inside that moving square as a 512-by-512 video, without the guide itself. Preserve the timing and scene content, follow the guide smoothly, hold the framing steady if the main subject briefly disappears, and do not alter anything except cropping and resizing." }, { "id": "Task_197_temporal_auto_white_balance_pumping_correction", "name": "Temporal Auto-White-Balance Pumping Correction", "modality": "tv2v", "prompt": "Remove temporal auto-white-balance pumping and color-cast flicker while preserving the real illumination changes, motion, scene content, framing, and timing." }, { "id": "Task_198_dashcam_video_super_resolution", "name": "Dashcam Video Super-Resolution", "modality": "tv2v", "prompt": "Restore this low-resolution dashcam video to a sharp 1280x720 video while preserving the original scene, colors, motion, timing, and all visible content." }, { "id": "Task_199_dashcam_video_compression_artifact_removal", "name": "Dashcam Video Compression Artifact Removal", "modality": "tv2v", "prompt": "Remove H.264 compression artifacts, including blockiness, ringing, and banding, while preserving the exact scene content, motion, colors, 1280x720 resolution, frame order, and timing." }, { "id": "Task_1_lits_liver_tumor_segmentation", "name": "LiTS Liver Tumor Segmentation", "modality": "tv2v", "prompt": "Highlight necrotic tumor core in red, peritumoral edema in yellow, and enhancing tumor in green throughout the brain FLAIR MRI video." }, { "id": "Task_20_crowd_flow_direction", "name": "crowd flow direction", "modality": "tv2v", "prompt": "Visualize the crowd flow by overlaying density, tracked head positions, short motion trails, and direction arrows on the video." }, { "id": "Task_234_action_conditioned_continuation", "name": "action_conditioned_continuation", "modality": "tv2v", "prompt": "Continue this video. The full clip shows the action: \"moving battery and bangle away from each other\". Generate the continuation that naturally and physically follows from the given beginning, keeping the same scene, objects, camera and hands consistent with the input." }, { "id": "Task_238_doodle_stroke_order_reconstruction", "name": "handwritten_character_stroke_order_reconstruction", "modality": "tv2v", "prompt": "Transform the start-point-guided, jump-step doodle progression into a smooth, frame-complete drawing animation. Begin at the indicated location and reconstruct the missing in-between motion while preserving the visible stroke order, every pen-up boundary, the relative drawing speed, and the final geometry." }, { "id": "Task_239_document_layout_role_visualization", "name": "document_layout_role_visualization", "modality": "tv2v", "prompt": "Visualize every official DocLayNet layout annotation on this unchanged page as an axis-aligned rectangular bounding box; these boxes mark region extents, not segmentation masks or reading-order links. Add roles one at a time in this explicit order: Section-header — red-orange (#F05028), temporary legend label at the top-left corner; Text — blue-violet (#5A5ADC), temporary legend label at the top-left corner; Page-footer — cyan (#00DCFF), temporary legend label at the top-left corner. During each role stage, add all identified boxes of that role and show one temporary legend label with the current role, color, and the number of boxes actually added in that stage. When the next stage begins, remove the old label but keep all earlier boxes. At the final stage, remove the temporary legend label and retain only all identified rectangular boxes. Do not change the page, infer order, add regions, or omit official annotations." }, { "id": "Task_23_video_outpainting", "name": "video_outpainting", "modality": "tv2v", "prompt": "Expand the visible video into the masked border regions while preserving the original content and temporal motion. The input video is 512x288 pixels and masked into a visible region of 342x288 pixels. The output video is cropped from its original resolution to 512x288 pixels." }, { "id": "Task_242_radar_precipitation_nowcasting", "name": "Radar Precipitation Nowcasting", "modality": "tv2v", "prompt": "The Input video shows 13 consecutive weather-radar maps of Vertically Integrated Liquid (VIL) over one fixed region, sampled every five minutes. VIL summarizes the radar-estimated amount of liquid water through the atmospheric column; the categorical colors represent increasing encoded VIL intensity. This is precipitation nowcasting: infer how the same storm cells move and evolve from the observed sequence, including changes in position, shape, growth, decay, splitting, and merging. Generate the next 12 VIL radar frames for the same grid and color scale, covering the following 60 minutes at five-minute intervals." }, { "id": "Task_248_category_conditioned_multi_object_tracking", "name": "category_conditioned_multi_object_tracking", "modality": "tv2v", "prompt": "Track every \"duck\" instance in the video. Draw a bounding box around each duck instance in every frame where it is visible, give each instance its own fixed color and instance number, keep the same color and number when an instance leaves and re-enters the view, and do not mark any object of other categories. Keep everything else in the video unchanged." }, { "id": "Task_252_vehicle_motion_state_annotation", "name": "vehicle_motion_state_annotation", "modality": "tv2v", "prompt": "Annotate the motion state of nearby visible vehicles in this driving video. In every frame, draw a bounding box around each vehicle whose projected bounding box covers more than 0.1% of the full image area, color the box red if the vehicle is stationary and green if it is moving, and display its current speed in km/h above the box. If eligible vehicle boxes overlap by at least 15% of the smaller box area, annotate only the nearer vehicle. Ignore smaller, distant, or materially overlapping farther vehicles. Do not annotate pedestrians, cyclists, or any non-vehicle object, and keep everything else in the video unchanged." }, { "id": "Task_254_depth_map_video_rendering", "name": "depth_map_video_rendering", "modality": "tv2v", "prompt": "Estimate the depth of every pixel in the video. Output the same video rendered as a per-frame depth map using a fixed color scale where warm colors are near and cool colors are far, render pixels with no valid depth as black, and keep the output aligned frame by frame with the input." }, { "id": "Task_255_vehicle_motion_state_annotation", "name": "vehicle_motion_state_annotation", "modality": "tv2v", "prompt": "Estimate the motion of every annotated vehicle in the driving video. In every frame, draw its 3D bounding box, show its current speed in km/h, and label its motion state as STOPPED or MOVING. Update the values as the motion changes, remove the overlay when the vehicle leaves the view, and keep everything else in the video unchanged." }, { "id": "Task_263_prompt_guided_single_object_3d_grounding", "name": "prompt_guided_single_object_3d_grounding", "modality": "tv2v", "prompt": "Highlight only the television monitor that is the only visible television monitor in this clip. Draw a clean red, temporally consistent oriented 3D bounding box around that same physical object wherever its visible box edges are supported by the video depth. Do not draw the box when the object is fully occluded or out of view, and keep everything else unchanged." }, { "id": "Task_264_robot_masked_action_visualization", "name": "robot_masked_action_visualization", "modality": "tv2v", "prompt": "Highlight the complete visible physical robot in green, including the robot arm body, gripper, joints, and physically attached cables. Preserve the original scene, camera view, timing, and every non-robot pixel. Do not highlight furniture, printed robot images, reflections, shadows, screens, rails, pillows, drawers, or unrelated objects. Do not infer or complete robot geometry that is invisible or outside the frame." }, { "id": "Task_266_analog_head_switching_noise_correction", "name": "analog_head_switching_noise_correction", "modality": "tv2v", "prompt": "Remove the analog head-switching noise and unstable tearing band at the bottom of the video while preserving the original scene, motion, framing, color, and timing." }, { "id": "Task_271_visual_prompt_mask_propagation", "name": "Visual Prompt Mask Propagation", "modality": "tv2v", "prompt": "A yellow dot on the first frame marks one target object. Segment that object throughout the video: in every frame where it is visible, overlay a semi-transparent red mask that exactly covers it, including after it disappears and reappears. Do not cover any other object, leave frames where it is invisible unmarked, and keep everything else in the video unchanged." }, { "id": "Task_275_action_conditioned_bimanual_manipulation_video_continuation", "name": "action_conditioned_bimanual_manipulation_video_continuation", "modality": "tv2v", "prompt": "Continue the video as the person continues gripping with the right hand, involving the two tongs, the plate, and the green mug." }, { "id": "Task_276_video_scene_text_replacement", "name": "video_scene_text_replacement", "modality": "tv2v", "prompt": "Replace the visible text content \"EASTERN FRESH\" with the literal text content \"SOUTHERN GROWERS\"; preserve all other visual content, motion, and timing." }, { "id": "Task_278_action-conditioned_gameplay_stutter_repair", "name": "action-conditioned_gameplay_stutter_repair", "modality": "tv2v", "prompt": "Repair the frozen interval at zero-based frames 48-55, inclusive. During this interval, the look-right control is held (key: L), so restore the missing character, camera, and scene motion. Preserve all other frames and return the same 81-frame, 30 fps, 1920x1080 timeline." }, { "id": "Task_285_segmentation_mask_correction", "name": "segmentation_mask_correction", "modality": "tv2v", "prompt": "The red mask overlaid on this video is inaccurate: its boundary may be too loose or too tight, it may jitter over time, drift onto the wrong object, or vanish on some frames. Correct it so that the red mask exactly and consistently covers the intended object in every frame where it is visible, and keep everything else in the video unchanged." }, { "id": "Task_291_celebv_hq_talking_head", "name": "celebv_hq_talking_head", "modality": "tv2v", "prompt": "Preserve the person's identity, facial details, speech motion, camera composition, and temporal timing. Draw a visible green tracking rectangle around the person's face/head only, excluding hair and body, and move it with the face in every frame. Do not blur, mask, or alter the background." }, { "id": "Task_296_sparse_frame_dance_motion_restoration", "name": "_sparse_frame_dance_motion_restoration", "modality": "tv2v", "prompt": "Restore smooth continuous dance motion in the Input. The Input preserves every fourth original frame and the final frame, while positions between those visible anchors repeat the preceding anchor. Fill the missing motion between anchors without changing the dancer, appearance, camera, background, frame rate, frame count, resolution, or timeline." }, { "id": "Task_297_gaze_primed_hand_object_interaction_phase_visualization", "name": "gaze_primed_hand_object_interaction_phase_visualization", "modality": "tv2v", "prompt": "Visualize the gaze-primed interaction endpoint associated with the knife. Highlight the left and right hands with semi-transparent cyan and magenta masks, respectively, and trace each mask with a solid outline in the same color. Display the associated object and current phase in large, clear text at the top. Directly below it, add a phase progression bar: gray represents context, amber represents gaze priming, and green represents pickup, with a white marker for the current frame. On the frame where gaze priming transitions into interaction, highlight the associated object with a semi-transparent yellow mask and a bold yellow bounding box. Show the object highlight only on that frame while preserving the original video timing and appearance elsewhere." }, { "id": "Task_298_droid_language_conditioned_robot_task_completion", "name": "droid_language_conditioned_robot_task_completion", "modality": "tv2v", "prompt": "Complete the robot task. Use the current camera image as the coordinate system: left and right are the left and right sides of this image, and up and down are its top and bottom. Move the green can from blue cabinet to green cup, moving toward the bottom of the current camera image once." }, { "id": "Task_306_laser_reflection_path_prediction", "name": "laser_reflection_path_prediction", "modality": "tv2v", "prompt": "The laser emitter lights up and starts firing an invisible beam. Predict the beam's full path: from the moment the emitter turns on, draw the beam propagating at constant speed, reflecting off each mirror with the angle of reflection equal to the angle of incidence, stopping when it is absorbed or leaves the scene, and lighting up the target if it is hit. Keep the traveled path visible and everything else in the scene unchanged." }, { "id": "Task_307_point_trajectory_tracking_visualization", "name": "point_trajectory_tracking_visualization", "modality": "tv2v", "prompt": "Track the query points marked at the beginning of the video through the whole clip. Draw each point as a solid colored dot with a fading trail while it is visible, switch it to a hollow circle at its true position while it is occluded, and keep each point's color constant so its identity never changes. Keep everything else in the video unchanged." }, { "id": "Task_308_repetitive_action_counting_annotation", "name": "repetitive_action_counting_annotation", "modality": "tv2v", "prompt": "Count the repetitions of the action in the video. Overlay a counter at the bottom that increments by one exactly when each action cycle completes according to the true cycle boundaries, with a progress ring that fills over the current cycle and shows a dash when no action is being performed. Keep everything else in the video unchanged." }, { "id": "Task_309_video_object_relation_annotation", "name": "Video Object Relation Annotation", "modality": "tv2v", "prompt": "Detect people and the relations between people in the video. Draw each annotated person's bounding box with a stable identity color and its person class plus identity number. During each annotated person-to-person relation interval, show a text tuple in subject-ID:predicate:object-ID order that appears and disappears exactly at the true start and end frames. Do not draw boxes or relation tuples for non-human objects. Do not draw relation arrows or connector lines. Keep everything else in the video unchanged." }, { "id": "Task_314_multi_object_bin_custody_and_occupancy_visualization", "name": "multi_object_bin_custody_and_occupancy_visualization", "modality": "tv2v", "prompt": "Visualize multi-object custody and bin occupancy throughout the input video. Overlay a stable identity-colored semi-transparent visible mask and a solid same-color contour on each object, and use a dashed same-color amodal contour only where the object is occluded. Use cyan outer and contact cues when an object is held, and a gold visible contour plus its designated bin-rim cue when it is correctly placed. Add a semi-transparent black status band along the bottom of the frame. In large clear text, display the active object and its current phase using TABLE / FREE, HELD / TRANSPORT, or CORRECT BIN. Use ACTIVE OBJECT: NONE when no object has yet become active. Directly below the text, draw a thin elapsed-phase timeline: gray represents TABLE / FREE, cyan represents HELD / TRANSPORT, and gold represents CORRECT BIN. Keep future frames dark and unfilled, and use a white marker for the current frame. Preserve the original camera, motion, timing, frame count, object identities, colors, shapes, and all scene pixels outside the annotation and status supports. Add no reference-dependent style, next-action cue, future-state cue, arrow, or side panel." }, { "id": "Task_315_egocentric_amodal_object_pose_continuity_visualization", "name": "Egocentric Amodal Object Pose Continuity Visualization", "modality": "tv2v", "prompt": "Preserve the input video's original RGB scene, viewpoint, timing, and geometry. For every modeled object, overlay its projected 3D mesh in a stable object-specific color: show the complete amodal mesh as a thin, lower-opacity dashed wireframe and the currently visible modal portion as a thicker, higher-opacity solid wireframe. Attach fixed-scale metric 6-DoF pose axes to each object, with X in red, Y in green, and Z in blue. Keep each object's identity, color, mesh, and axes temporally consistent through occlusion, disappearance, and reappearance. Apply overlays only to modeled objects; do not annotate hands or other scene content. Output only the overlaid video without labels, legends, captions, identifiers, or any other added text." }, { "id": "Task_316_bimanual_object_mesh_contact_state_visualization", "name": "bimanual_object_mesh_contact_state_visualization", "modality": "tv2v", "prompt": "Draw frame-aligned, color-coded outlines and joint lines over the person's left and right hands, and fill each outline with a translucent 3D surface overlay. Draw the same kind of outline and translucent 3D surface overlay, plus XYZ orientation axes, over the specific rigid object selected for this sample—the object whose proximity/contact relationship with the hands is being shown. Using fixed geometric distance rules, highlight only the hand/object surface areas that are near or touching. Keep the left hand, right hand, and that same object correctly identified and aligned in every frame, including while partly hidden. Use these RGB mask colors before transparency compositing exactly: left hand (020,120,255) | right hand (255,165,020) | selected rigid object (080,210,060) | left-hand proximity area on either surface (030,235,255) | left-hand contact area on either surface (235,030,255) | right-hand proximity area on either surface (255,235,040) | right-hand contact area on either surface (255,055,020) | selected-object area near both hands, with neither touching (210,255,080) | selected-object area touching at least one hand while near both (255,255,255)." }, { "id": "Task_317_rgb_video_to_publisher_relative_depth", "name": "Rgb Video To Publisher Relative Depth", "modality": "tv2v", "prompt": "Convert this RGB video segment into the publisher-style relative-depth appearance video of the same scene. Preserve the viewpoint, objects, motion, frame order, and the publisher's continuous color mapping. Keep every segment frame aligned one-to-one. The colors encode relative depth appearance, not metric distance or sensor ground truth. Do not crop, recolor, sharpen, threshold, normalize per frame, add annotations, or predict future frames." }, { "id": "Task_318_clean_reference_guided_video_lens_flare_removal", "name": "clean_reference_guided_video_lens_flare_removal", "modality": "tv2v", "prompt": "Remove the temporally varying lens flare from the input video. Preserve the original timing, framing, resolution, scene content, and motion, and return the corresponding clean video." }, { "id": "Task_31_causal_completion", "name": "Causal Completion", "modality": "tv2v", "prompt": "The input video begins at a traffic collision. Reconstruct the 16 consecutive frames immediately before impact, in chronological order." }, { "id": "Task_325_ditto_local_video_editing", "name": "ditto_local_video_editing", "modality": "tv2v", "prompt": "Remove the small floating holographic blueprint of the building project hovering near the crane." }, { "id": "Task_327_video-delighting-material-recovery", "name": "video-delighting-material-recovery", "modality": "tv2v", "prompt": "Convert the input video into a frame-aligned diffuse-reflectance material video. Preserve the camera motion, geometry, frame order, and underlying material colors while removing illumination, cast shadows, and view-dependent highlights. Encode valid surface pixels as clipped linear RGB reflectance. Set pixels without valid diffuse-reflectance data to black; reflective or transparent materials may appear dark or flattened. Do not produce photorealistic relighting." }, { "id": "Task_331_robot_tool_target_semantic_role_highlighting", "name": "robot_tool_target_semantic_role_highlighting", "modality": "tv2v", "prompt": "Highlight the manipulated tool in orange and the interaction target in blue. Preserve the robot motion, object poses, camera, scene, timing, and all other content." }, { "id": "Task_332_raw_physics_video_to_goal_force_causal_overlay_visualization", "name": "Raw Physics Video To Goal Force Causal Overlay Visualization", "modality": "tv2v", "prompt": "Predict the future motion in this scene. Continue the observed clip while preserving the scene, camera, and appearance." }, { "id": "Task_334_prompt_guided_single_view_multishot_recinematography", "name": "Prompt Guided Single View Multishot Recinematography", "modality": "tv2v", "prompt": "Create one synchronized 81-frame, 15-fps target video from the input, following this 2-shot plan.\n\nShot 1, frames 1–36:\nContextual wide performer view, slightly low angle.\nUse a smooth compound tracking move with depth and lateral change while noticeably reorienting the view with a gradual acceleration.\nHard cut before frame 37.\n\nShot 2, frames 37–81:\nContextual wide performer view, eye-level angle.\nUse a smooth lateral track toward camera-left while noticeably reorienting the view with a gradual ease-out.\n\nPreserve subject identity, scene, action, chronology, and frame timing; change only camera viewpoint, framing, and movement. Keep one full-frame view throughout; use only the listed hard cuts. No invented or reordered events; no duplicated, dropped, or interpolated frames; no slow motion, mosaics, split screens, option grids, labels, borders, or UI." }, { "id": "Task_335_tone_mapped_hdr_to_rich_color_short_exposure_rendering", "name": "tone_mapped_hdr_to_rich_color_short_exposure_rendering", "modality": "tv2v", "prompt": "Convert the bright, flat tone-mapped HDR video into a natural short-exposure rendering with richer color, stronger contrast, and controlled highlights. Preserve the scene geometry, motion, timing, scene identity, and camera trajectory." }, { "id": "Task_336_mask-guided_video_object_segmentation", "name": "mask-guided_video_object_segmentation", "modality": "tv2v", "prompt": "Segment the object indicated by the semi-transparent red mask in the first frame throughout the video. Output a binary mask video with the target object in white and the background in black in every frame." }, { "id": "Task_342_atmospheric_turbulence_restoration", "name": "atmospheric_turbulence_restoration", "modality": "tv2v", "prompt": "Remove atmospheric turbulence distortion from this video and restore a turbulence-free video. Correct spatially varying blur and local nonlinear geometric warping while preserving the original scene content, camera motion, subject motion, frame order, timing, framing, resolution, colors, textures, and illumination; do not crop, globally stabilize, recolor, retime, add, or remove anything." }, { "id": "Task_347_temporal_road_structure_bev_reconstruction", "name": "Temporal Road Structure Bev Reconstruction", "modality": "tv2v", "prompt": "From the PandaSet front-right camera driving video, generate a frame-synchronized, ego-centred bird's-eye semantic map of the road structure observed so far in the clip. Accumulate only input-camera-observed static structure over time; do not fill or infer unobserved regions. Use fixed colors: Road dark gray, Lane Line Marking yellow, Stop Line Marking red, Other Road Marking white, Sidewalk cyan-blue, and Driveway green. Show observed static support outside these six classes in dark charcoal and cells with no retained static observation in black. Keep vehicle forward pointing toward the top of the map, and match the input frame count and frame rate." }, { "id": "Task_34_motion_transfer", "name": "motion transfer", "modality": "tv2v", "prompt": "Render the primary person's frame-aligned articulated motion on a neutral background with orange joints and gray anatomical bones. Preserve source timing and one stable connected body topology; reinitialize only at real camera cuts, and leave a cut-delimited shot with no supported person empty. Keep tracking the same person through occlusion. Do not switch people, bridge cuts, draw motion trails, or connect non-anatomical joints." }, { "id": "Task_352_support_relation_loss_falling_onset_annotation", "name": "Support Relation Loss Falling Onset Annotation", "modality": "tv2v", "prompt": "Track the cyan-outlined target object through the complete video. Label it SUPPORTED while another object physically supports it, switch to SUPPORT LOST at the first persistent loss of that support, and switch to FALLING at the first frame of sustained downward motion." }, { "id": "Task_353_domino_chain_trigger_order_reconstruction", "name": "domino_chain_trigger_order_reconstruction", "modality": "tv2v", "prompt": "Number the participating chain objects in their true trigger order. Keep each number visible after triggering, briefly highlight the object when it is triggered, leave objects that never trigger unnumbered, and give objects first triggered in the same frame the same rank." }, { "id": "Task_354_instruction_guided_video_editing", "name": "instruction_guided_video_editing", "modality": "tv2v", "prompt": "convert to black and white" }, { "id": "Task_355_reference_guided_isolated_sign_word_caption_overlay", "name": "reference_guided_isolated_sign_word_caption_overlay", "modality": "tv2v", "prompt": "Preserve the complete Input video, including its timing, framing, and scene. Overlay the exact English caption 'LOOK' as a readable bottom caption throughout the video. Do not add, remove, or alter anything outside that caption overlay." }, { "id": "Task_357_blind_bitstream_corrupted_video_recovery", "name": "Blind Bitstream Corrupted Video Recovery", "modality": "tv2v", "prompt": "Restore this bitstream-corrupted video by removing blocking, color corruption, spatial displacement, smearing, and temporal propagation artifacts while preserving the original scene content, motion, framing, frame order, and timing." }, { "id": "Task_36_thermal_video", "name": "thermal video", "modality": "tv2v", "prompt": "Generate the corresponding paired thermal observation for this visible-light sequence." }, { "id": "Task_372_tartanground_front_rgb_to_fixed_scale_depth_colormap", "name": "tartanground_front_rgb_to_fixed_scale_depth_colormap", "modality": "tv2v", "prompt": "Convert this front-camera RGB video into a synchronized color-coded metric-depth video. Use one fixed distance scale in every frame: 0.5 m is dark purple (RGB 48, 18, 59), 10 m is blue (RGB 70, 102, 221), 20 m is cyan (RGB 42, 185, 238), 30 m is green (RGB 47, 241, 155), 40 m is yellow-green (RGB 161, 253, 61), 50 m is yellow (RGB 236, 209, 58), 60 m is orange (RGB 251, 129, 34), 70 m is red (RGB 210, 49, 5), and 80 m is dark red (RGB 122, 4, 3). Assign every depth between these points the intermediate color at the same relative position along this continuous scale, clamp nearer or farther valid depths to the endpoint colors, render invalid or non-positive depth as black, and preserve viewpoint, pixel alignment, frame order, frame rate, and timing." }, { "id": "Task_373_synchronized_multiview_pinhole_to_robot_fisheye_video_synthesis", "name": "Synchronized Multiview Pinhole To Robot Fisheye Video Synthesis", "modality": "tv2v", "prompt": "Transform the front-facing pinhole camera video into the corresponding robot fisheye camera video." }, { "id": "Task_374_temporal_disocclusion_region_visualization", "name": "temporal_disocclusion_region_visualization", "modality": "tv2v", "prompt": "Visualize how scene visibility changes over time. For each consecutive frame pair, use the left panel for source pixels: color pixels that remain visible green (RGB 64, 200, 64), pixels that become occluded or move out of view red (RGB 220, 64, 64), and invalid-depth pixels gray (RGB 96, 96, 96). Use the right panel for destination pixels: color pixels that remain visible green (RGB 64, 200, 64), newly visible pixels revealed by disocclusion blue (RGB 64, 160, 240), and invalid-depth pixels gray (RGB 96, 96, 96). Render both panels of the first target frame in dark gray (RGB 24, 24, 24) to indicate NO_PREDECESSOR. Preserve the original timeline." }, { "id": "Task_376_era5_500hpa_geopotential_to_2m_temperature_translation", "name": "era5_500hpa_geopotential_to_2m_temperature_translation", "modality": "tv2v", "prompt": "Translate this 16-step ERA5 500 hPa geopotential-height field video into the synchronized 2 m air-temperature field video at the exact same UTC timestamps and grid. Preserve the time order, map projection, geographic extent, resolution, coastlines, and frozen visualization conventions. Do not forecast future timestamps, shift the window, invent weather events, or copy numeric target values into text." }, { "id": "Task_378_bounded_compression_degradation_video_restoration", "name": "Bounded Compression Degradation Video Restoration", "modality": "tv2v", "prompt": "Remove the bounded H.264 compression degradation from this video segment. Restore the same scene, objects, motion, colors, geometry, frame order, frame rate, resolution, and timing without adding or removing content." }, { "id": "Task_380_dyadic_mouse_social_behavior_role_visualization", "name": "dyadic_mouse_social_behavior_role_visualization", "modality": "tv2v", "prompt": "Visualize the resident-intruder mouse interaction in this clip. Return the same top-view video with identity-consistent resident and intruder seven-keypoint skeletons, mouse heading and resident-to-intruder direction arrows, and the frame-aligned behavior label attack, investigation, mount, or other." }, { "id": "Task_381_subject_aware_background_bokeh_editing", "name": "subject_aware_background_bokeh_editing", "modality": "tv2v", "prompt": "Keep the subject described as \"woman shooting a basketball\" sharp throughout the video and apply a stable shallow-depth-of-field blur to the background only." }, { "id": "Task_395_robust_moving_foreground_segmentation", "name": "robust_moving_foreground_segmentation", "modality": "tv2v", "prompt": "Identify every genuine foreground object that moves at any point in the input video. Once an object has moved, keep it covered by a semi-transparent green mask even if it later stops. Do not mask objects that remain stationary throughout the entire input video. Ignore cast shadows, lighting changes, waving vegetation, water, weather, dynamic background, sensor noise, and camera jitter. Preserve all other visible pixels and the declared playback timing. Black padding marks pixels outside the source dataset's official valid annotation region; leave that padding unchanged." }, { "id": "Task_402_compositional_multi_instruction_video_editing", "name": "Compositional Multi Instruction Video Editing", "modality": "tv2v", "prompt": "Replace the blue chef's jacket worn by the man with a white double-breasted chef's coat.\nRemove the glasses worn by the man.\nReplace the dark kitchen cabinetry behind the man with white subway tile walls." }, { "id": "Task_406_paper_folding_punch_unfolding", "name": "paper_folding_punch_unfolding", "modality": "tv2v", "prompt": "The paper has been folded step by step and punched. Continue the video: unfold the paper step by step in the exact reverse order of the folds, keeping each unfolding motion smooth, until it lies completely flat and reveals the full pattern of holes, with every hole placed at the mirror positions determined by the folds it passed through. Keep everything else in the scene unchanged." }, { "id": "Task_407_circuit_connectivity_reasoning", "name": "circuit_connectivity_reasoning", "modality": "tv2v", "prompt": "One switch in the circuit closes. Predict the result: from the moment the switch finishes closing, light up exactly the bulbs that now have a complete path from the battery through closed switches, turning them on one by one in order of their distance from the battery, and keep every bulb without a complete path dark. Keep everything else in the diagram unchanged." }, { "id": "Task_408_buoyancy_float_sink_prediction", "name": "Buoyancy Float Sink Prediction", "modality": "tv2v", "prompt": "The object is released above the liquid. Predict what happens when it enters: using the material and liquid densities shown in the legend, make it float at the correct waterline with the submerged fraction equal to the density ratio, hover if the densities match, or sink to the bottom, with the liquid level rising by the displaced volume and ripples settling. Keep everything else in the scene unchanged." }, { "id": "Task_409_communicating_vessels_water_level", "name": "communicating_vessels_water_level", "modality": "tv2v", "prompt": "Water is being poured into one branch of the connected vessels at a constant rate. Continue the process: let the water flow through the base so that all branches rise together toward the same height, with narrower branches rising faster but never ending higher, and when pouring stops, settle every branch at exactly the same level determined by the total volume. Keep everything else in the scene unchanged." }, { "id": "Task_414_event_camera_object_6d_pose_mask_overlay_visualization", "name": "event_camera_object_6d_pose_mask_overlay_visualization", "modality": "tv2v", "prompt": "Given event-camera frames that visualize brightness changes as sparse light and dark edges, contours, and corners rather than normal RGB images, render the publisher-provided object mask, 3D bounding box, and 6D pose over each frame without any in-frame text labels or legend. Use one fixed object-identity color for each mask and its matching 3D box across all frames: object 1 amber RGB(255,194,66), object 2 cyan RGB(62,207,255), object 3 pink RGB(255,99,146), object 4 light green RGB(155,235,120), and object 5 purple RGB(198,142,255). Independently draw the 6D pose axes as +X red RGB(255,70,70), +Y green RGB(80,235,105), and +Z blue RGB(70,145,255) in the fixed camera coordinate system, with a white origin dot outlined in black." }, { "id": "Task_418_daytime_visible_to_clean_longwave_infrared_translation", "name": "Daytime Visible To Clean Longwave Infrared Translation", "modality": "tv2v", "prompt": "Translate this daytime 0.64-micrometer visible weather sequence into the co-temporal 10.3-micrometer clean longwave-infrared brightness-temperature video. Preserve the ABI fixed grid, scan timing, cloud geometry, motion, and temporal order." }, { "id": "Task_419_mrms_radar_precipitation_nowcasting", "name": "Temporal Posterization Restoration", "modality": "tv2v", "prompt": "Forecast the next 48 minutes of radar-reflectivity evolution from the preceding 48 minutes, preserving geographic alignment while evolving storm motion, growth, decay, shape, and intensity. Colors encode reflectivity intensity: near-black is below 5 dBZ; cyan, teal, and green are 5-25 dBZ; yellow-green and yellow are 25-35 dBZ; orange and red are 35-55 dBZ; magenta and purple are 55-70 dBZ; white is at least 70 dBZ; and dark gray means missing or no coverage." }, { "id": "Task_420_rgb_to_event_camera_motion_translation", "name": "Rgb To Event Camera Motion Translation", "modality": "tv2v", "prompt": "Translate the synchronized RGB video into the event-camera motion-pulse representation. Render positive brightness-change events in cyan and negative brightness-change events in red, preserve their timing and locations, and leave unchanged regions dark." }, { "id": "Task_421_synchronized_top_to_wrist_garment_manipulation_view_translation", "name": "synchronized_top_to_dual_wrist_garment_manipulation_view_translation", "modality": "tv2v", "prompt": "Translate this top-view video of a dual-arm robot folding a garment into the synchronized left-wrist RGB camera view from the same episode and timeline. Preserve the garment state, robot motion, frame order, complete duration, and native 30 FPS timing." }, { "id": "Task_423_humanoid_foot_contact_slip_visualization", "name": "Humanoid Foot Contact Slip Visualization", "modality": "tv2v", "prompt": "Overlay the estimated ground as a visible horizontal band. Mark left-foot contact with a cyan filled pad and right-foot contact with an orange filled pad; use a hollow ring when either foot is in swing. Connect active contacts with a yellow support segment, or show a yellow halo for single-foot support. Project the kinematic body-center proxy onto the ground as a magenta cross connected by a magenta guide line. When a contacting foot slides, draw a red trailing path. Preserve the humanoid motion, framing, frame order, and native 120 FPS timing." }, { "id": "Task_424_dynamic_snow_video_removal", "name": "dynamic_snow_video_removal", "modality": "tv2v", "prompt": "Remove the synthetic snow and haze from this video and restore the corresponding clear scene. Preserve scene content, object and camera motion, framing, frame order, and timing." }, { "id": "Task_425_thermal_to_visible_rgb_video_translation", "name": "thermal_to_visible_rgb_video_translation", "modality": "tv2v", "prompt": "Translate this synchronized thermal video into the corresponding visible-light RGB video of the same scene and timeline. Preserve scene geometry, people and vehicle motion, camera motion, frame order, and timing." }, { "id": "Task_428_same_time_background_and_scenery_replacement", "name": "Same Time Background And Scenery Replacement", "modality": "tv2v", "prompt": "Edit only the background and static scenery. Replace the original Procgen scene with a stylized 2D cartoon cold snowy mountain landscape, matching the original video's simple animated aesthetic. Use pale blue-gray sky with thin cold haze. Build overlapping jagged blue-gray mountains with white snow peaks. Shape the middle distance as broad snow-covered slopes. Use continuous light snowfield with shallow blue shadows. Add dark evergreen trees with snow tips, ice boulders and snow-covered storage boxes. Remove the source scene's original background palette and static scenery silhouettes while leaving every character and interactive object unchanged. Keep all characters and interactive objects unchanged; preserve their positions, motion, layout, camera timing, and all 81 moments at 15 FPS (5.4 seconds)." }, { "id": "Task_431_egocentric_gaze_scanpath_state_reconstruction", "name": "Egocentric Gaze Scanpath State Reconstruction", "modality": "tv2v", "prompt": "Reconstruct the camera wearer's measured gaze scanpath from this egocentric video. Return the same video with the current gaze point and its 12-frame fading trail overlaid directly on each corresponding RGB frame. Preserve the scene, frame order, duration, and camera motion. Use the same cyan color for every gaze point and connecting segment; fade older trail elements by reducing opacity, not by changing color. Do not encode gaze type or confidence using color. Do not replace the scene with a black background, render a saliency heatmap, or add text or legends." }, { "id": "Task_440_deformable_object_strain_visualization", "name": "deformable_object_strain_visualization", "modality": "tv2v", "prompt": "Visualize the deformable object's strain over time: overlay a strain-magnitude heatmap on the deformed region, trace the deformed boundary, and show the applied force direction while preserving the original motion, framing, and timing." }, { "id": "Task_446_instruction_guided_season_environment_editing", "name": "Instruction Guided Season/environment Editing", "modality": "tv2v", "prompt": "Change the weather into: Summer golden tone – Infuse the scene with warm summer gold: strong amber sunlight, golden-hour glow on surfaces, deep warm shadows, and saturated yellow-orange ambient reflections." }, { "id": "Task_454_libero_language_conditioned_video_continuation", "name": "Libero Language Conditioned Video Continuation", "modality": "tv2v", "prompt": "turn on the stove and put the frying pan on it" }, { "id": "Task_457_clevrer_counterfactual_event_visualization", "name": "Clevrer Counterfactual Event Visualization", "modality": "tv2v", "prompt": "Watch the complete synthetic-physics video and answer the official counterfactual question. For every listed candidate event, determine whether it would happen if the object specified in the question were removed. Each candidate event below has a one-based Event number. Produce the same complete video with a compact static bottom panel containing one large numbered square for each Event. Color a square green when that Event happens in the official what-if outcome and red when it does not happen. Put only the number inside each square; do not add status words. Treat the colors as final official what-if outcomes, not observed-video timestamps. Do not change the underlying scene, animate the colors, or invent event times.\n\nQuestion: If the cube is removed, what will happen?\nCandidate events:\nEvent 1: The cylinder and the purple object collide\nEvent 2: The yellow object collides with the purple object\nEvent 3: The cylinder collides with the yellow object" }, { "id": "Task_471_moving_cable_motion_segmentation", "name": "Moving Cable Motion Segmentation", "modality": "tv2v", "prompt": "Highlight only the moving cable or hose pixels in this video using a translucent green mask color (RGB [0, 255, 0]) while preserving the original scene, timing, and background." }, { "id": "Task_47_depth_estimation", "name": "Depth Estimation", "modality": "tv2v", "prompt": "Create a frame-aligned depth visualization while preserving the Input video's frame order, camera viewpoint, geometry, and temporal consistency. Within each clip, show the nearest valid surfaces in dark purple or blue. As distance from the camera increases, transition smoothly through cyan, green, and yellow, and show the farthest valid surfaces in dark red. Show missing or invalid depth in black." }, { "id": "Task_484_accelerated_cardiac_cine_mri_reconstruction", "name": "Accelerated Cardiac Cine Mri Reconstruction", "modality": "tv2v", "prompt": "Given an accelerated cardiac cine MRI video reconstructed from retrospectively undersampled multi-coil k-space with zero-filling artifacts, recover the clean fully sampled cardiac cine MRI video. Preserve the heart anatomy, cardiac motion across the full cine cycle, and temporal consistency while removing undersampling artifacts." }, { "id": "Task_48_rehabilitation_action_assessment", "name": "Rehabilitation Exercise Assessment", "modality": "tv2v", "prompt": "Correct the V-to-W arm movement movement in the input video. Specifically, during the V-to-W transition, bend the left elbow more; during the V-to-W transition, bend the right elbow more. Complete the movement smoothly and return under control. Preserve the same person, camera viewpoint, clothing, lighting, background, and overall video style." }, { "id": "Task_493_hmi_vector_magnetic_field_to_aia_171_euv_translation", "name": "Hmi Vector Magnetic Field To Aia 171 Euv Translation", "modality": "tv2v", "prompt": "Translate this HMI Bx/By/Bz magnetic-field video into the corresponding AIA 171Å EUV coronal video at the nearest real observation within 60 seconds of each HMI observation, using the fixed task normalization. Each frame represents a real observation; intervals may be irregular and are at most one hour within the same annual source sequence. Preserve the paired observation order. Do not forecast, interpolate, repeat frames, cross an annual source sequence, or synthesize solar activity." }, { "id": "Task_495_synthetic_room_camera_history_to_immediate_future_video", "name": "Synthetic Room Camera History To Immediate Future Video", "modality": "tv2v", "prompt": "Continue the camera motion through the same synthetic room for the next 81 recorded observations, preserving object geometry, colors, and viewpoint continuity." }, { "id": "Task_496_locomotion_rgb_history_to_immediate_future_video", "name": "Locomotion Rgb History To Immediate Future Video", "modality": "tv2v", "prompt": "Continue the same locomotion episode with the immediately following segment, preserving the body, camera, background, and temporal continuity." }, { "id": "Task_502_tartanair_depth_heatmap_overlay_editing", "name": "Tum Rgbd Depth Heatmap Overlay Editing", "modality": "tv2v", "prompt": "Overlay the aligned metric depth on this RGB video using the fixed task-wide heatmap: render near valid surfaces red, progress through orange, yellow, and cyan, and render far valid surfaces blue. Keep invalid-depth pixels unchanged and preserve the video's complete scene, geometry, timing, frame rate, and audio state." }, { "id": "Task_504_accident_driver_attention_prediction", "name": "Accident Driver Attention Prediction", "modality": "tv2v", "prompt": "Watch the driving video and predict where a driver's attention would go. Overlay a semi-transparent attention heatmap on every frame using the fixed colormap, shifting the heat toward emerging hazards as they appear, approach, and unfold, matching the true recorded driver fixations. Keep everything else in the video unchanged." }, { "id": "Task_505_collision_event_causal_annotation", "name": "Collision Event Causal Annotation", "modality": "tv2v", "prompt": "Watch the simulated scene and annotate its collision events. Give each object a constant identity-colored outline that fades in shortly before it collides. On each collision frame, highlight the image regions of both colliding objects. Use the visible object contours to place these approximate region annotations; do not mark an exact physical contact point. Keep the original scene, timing, viewpoint, and motion unchanged. If nothing collides, add no collision annotations." }, { "id": "Task_507_monocular_video_to_3d_human_mesh_recovery", "name": "Monocular Video To 3D Human Mesh Recovery", "modality": "tv2v", "prompt": "Recover the principal person's temporally coherent 3D body mesh throughout the monocular video and render only the predicted shaded light-gray mesh on a black background in the original camera view. Preserve the source framing, timing, and camera projection, but do not retain the source person, scene, a binary mask, cut-out, skeleton, global-view animation, or audio." }, { "id": "Task_531_synchronized_hand_object_novel_view_video_generation", "name": "Synchronized Hand Object Novel View Video Generation", "modality": "tv2v", "prompt": "Show the same hand-object interaction from the specified nearby camera, preserving the action, objects, capture interval and frame order. Use the input camera as the origin with positive x right, y down and z forward. The input pixel intrinsics (fx, fy, cx, cy) are (931.966675, 935.182617, 670.530777, 380.865140), and its Brown lens distortion (k1, k2, p1, p2) is (-0.365926437, 0.108551802, 0.001699029, -0.001138564). Place the target camera center at (0.282939, -0.003722, 0.085070) meters in that coordinate system. Starting with camera axes parallel to the input axes, apply right-hand-rule rotations of (5.659742, -18.290201, -10.888386) degrees about the fixed input x, then y, then z axes. Render the full 1280 by 720 pixel view at 30 frames per second with exactly the input frame count. Use pixel intrinsics (fx, fy, cx, cy) = (885.953552, 882.357178, 679.026031, 350.505366) and Brown lens distortion (k1, k2, p1, p2) = (-0.383007891, 0.114276085, -0.002540514, -0.005864209), with zero skew and no additional distortion terms. Preserve the scene appearance and natural occlusions of that viewpoint." }, { "id": "Task_535_laparoscopic_surgical_smoke_removal", "name": "Laparoscopic Surgical Smoke Removal", "modality": "tv2v", "prompt": "Remove the visible surgical smoke from this laparoscopic video while preserving the same tissue, instruments, camera motion, framing, colors, textures, frame order, and timing. Do not invent anatomy, instruments, or surgical events." }, { "id": "Task_549_pedestrian_pose_depth_occlusion_visualization", "name": "Pedestrian Pose Depth Occlusion Visualization", "modality": "tv2v", "prompt": "Preserve the original video and draw each pedestrian's bounding box and visible 22-keypoint skeleton. Use green for visible pedestrians (RGB 0,255,0), yellow for partially occluded pedestrians (RGB 255,255,0), and red for heavily occluded pedestrians (RGB 255,0,0). Keep the official 3D depth statistic in metadata only. Do not add text labels." }, { "id": "Task_557_camouflaged_animal_bbox_motion_state_visualization", "name": "Rgb Depth Thermal Target Tracking Overlay", "modality": "tv2v", "prompt": "Locate the camouflaged animal in every frame and draw the official tight bounding box around it. Color the box orange during locomotion, cyan during subtle in-place motion, and gray while still. Keep the sampled frame order, 5 FPS display cadence, scene pixels, framing, and geometry unchanged outside the box stroke. Use no text, mask, trajectory, interpolation, or invented animal position." }, { "id": "Task_577_wild_sdg_synthetic_rgb_to_publisher_depth_pseudolabel_video", "name": "Wild Sdg Synthetic Rgb To Publisher Depth Pseudolabel Video", "modality": "tv2v", "prompt": "Convert the complete input video into a frame-aligned visualization of the publisher's estimated depth pseudolabels. Preserve the camera view, frame order, and timing. Use one fixed depth scale for the entire sequence, with nearer valid surfaces lighter and farther valid surfaces darker; show invalid depth in black. The visualization represents estimated relative depth appearance, not sensor ground truth." }, { "id": "Task_578_synthetic_human_rgb_to_publisher_2d_pose_projection_overlay", "name": "Synthetic Human Rgb To Publisher 2d Pose Projection Overlay", "modality": "tv2v", "prompt": "Overlay the person's projected whole-body pose on the original video, keeping the original timing and framing. Draw the body and limb connections, hands, and facial landmarks. This projection includes joints that may be occluded; it is not a visibility or confidence annotation." }, { "id": "Task_580_robot_rgb_to_publisher_depth_video", "name": "Robot Rgb To Publisher Depth Video", "modality": "tv2v", "prompt": "Convert this RGB video of a Panda robot into the publisher-style grayscale depth-appearance video of the same scene. Use the publisher's observed polarity: within each frame, nearer visible surfaces are darker and farther visible surfaces are lighter; gray values express depth ordering, not metric distance. Preserve the viewpoint, objects, motion, frame order, timing, continuous gray levels, and native soft/noisy boundaries and invalid/background values. This is a continuous depth map, not a binary foreground mask. Do not threshold, sharpen, recolor, normalize per frame, or add annotations." }, { "id": "Task_585_laia_trajectory_preserving_clear_to_hard_rain_driving_translation", "name": "Laia Trajectory Preserving Clear To Hard Rain Driving Translation", "modality": "tv2v", "prompt": "Transform this clear-weather driving video into the corresponding heavy-rain driving video. Keep the road scene, all vehicles and their trajectories, camera motion, geometry, frame count, frame order, and timing exactly unchanged." }, { "id": "Task_615_video_gamma_transfer_mismatch_correction", "name": "Video Gamma Transfer Mismatch Correction", "modality": "tv2v", "prompt": "Correct the video's gamma/transfer-function mismatch that makes midtones and shadows unnaturally dark. Restore natural tones and colors while preserving all scene content, motion, timing, framing, resolution, and audio state." }, { "id": "Task_617_prompt_conditioned_analog_clock_time_shift", "name": "Prompt Conditioned Analog Clock Time Shift", "modality": "tv2v", "prompt": "Shift this analog clock backward by 15 minutes. Keep the clock face, colors, frame timing, and ticking speed unchanged." }, { "id": "Task_62_video_depth_enhancement", "name": "Video Depth Enhancement", "modality": "tv2v", "prompt": "Generate the synchronized right-eye stereo view of this video. Preserve the scene, subjects, motion, timing, lighting, and image style while applying the same camera-baseline shift shown by the paired stereo capture." }, { "id": "Task_630_av2_rgb_to_dynamic_lidar_geometry", "name": "Av2 Rgb To Dynamic Lidar Geometry", "modality": "tv2v", "prompt": "Transform this 5-second, 50-frame driving video into a synchronized sparse 3D geometry video. For each RGB frame, output a black frame containing only sensor-measured LiDAR dots that line up with the same visible roads, curbs, buildings, vehicles, and other surfaces. Follow the legend embedded in the Input HUD: color each dot by camera distance on the same fixed 1-to-80-meter TURBO scale, from purple/blue nearby through green/yellow to orange/red far away. Keep ordinary static returns as plain depth-colored dots. For a moving traffic actor, place a magenta halo around its real depth-colored dots and draw a short cyan arrow in its measured direction of motion. Preserve all 50 frames, the exact camera viewpoint, resolution, order, and 10 FPS timing. Keep the output genuinely sparse: never invent dots, fill gaps or surfaces, interpolate missing measurements, or create a dense depth map." }, { "id": "Task_639_first_frame_query_point_trajectory_visualization", "name": "First Frame Query Point Trajectory Visualization", "modality": "tv2v", "prompt": "Track the surface point indicated by the ring in the first frame through the entire video. Return the same scene, framing, frame order, and timing with a small cyan marker and short trajectory trail at that point in every frame where it is visible; hide the marker while the point is occluded or outside the view, and restore the same point when it becomes visible again." }, { "id": "Task_647_road_pothole_binary_mask_video_segmentation", "name": "Road Pothole Binary Mask Video Segmentation", "modality": "tv2v", "prompt": "Segment every road pothole in every frame. Return a temporally aligned binary mask video with pothole pixels white and all other pixels black. Preserve frame count, order, timing, viewpoint, and geometry. Return no RGB overlay, boxes, text, or audio." }, { "id": "Task_64_object_permanence_repair", "name": "object permanence repair", "modality": "tv2v", "prompt": "Restore the single object that disappears during the middle of the video. Use its visible appearance and motion before and after the gap to keep the same object present throughout the missing interval with a plausible, continuous trajectory. Leave all other objects, the background, camera motion, framing, timing, and video content unchanged." }, { "id": "Task_656_radial_chromatic_aberration_correction", "name": "Radial Chromatic Aberration Correction", "modality": "tv2v", "prompt": "Remove the radial chromatic aberration and red/blue edge fringing while preserving scene content, geometry, motion, framing, frame order, and timing." }, { "id": "Task_662_contact_response_timing_repair", "name": "Contact Response Timing Repair", "modality": "tv2v", "prompt": "Repair the contact-response timing so the passive object begins moving only when the moving object first touches it. Keep the initial state, objects, camera, duration, appearance, and all other motion unchanged." }, { "id": "Task_663_robot_trajectory_gap_completion", "name": "Language Conditioned Robot Trajectory Gap Completion", "modality": "tv2v", "prompt": "Reconstruct the true robot manipulation in primary camera images0 for missing frames 20 through 29 (0-indexed). The missing interval is shown as neutral gray. Keep every other frame unchanged." }, { "id": "Task_670_textured_animation_to_skeletal_motion_visualization", "name": "Textured Animation To Skeletal Motion Visualization", "modality": "tv2v", "prompt": "Convert the textured animated object into a skeletal motion visualization. Show its joints and connecting bones inside a white three-dimensional coordinate grid from a fixed elevated oblique viewpoint, looking diagonally downward from one side. Keep this skeleton-view camera fixed throughout the clip; it is independent of the RGB input camera, so the skeleton's projected orientation need not match the input image. Preserve the same articulated action, motion phase, frame rate, and complete selected timeline segment. Remove the textured surface and appearance; do not add an appearance reference." }, { "id": "Task_673_droid_local_temporal_corruption_restoration", "name": "Droid Local Temporal Corruption Restoration", "modality": "tv2v", "prompt": "Restore the original temporal order of this six-second DROID robot manipulation video. The 180-frame input is split at zero-based frame boundaries 0, 18, 48, 78, 108, 180 (approximately 0.00, 0.60, 1.60, 2.60, 3.60, 6.00 seconds at 30 FPS). Use these segments to reconstruct a continuous chronological sequence from the same camera without changing its appearance." }, { "id": "Task_674_robot_manipulation_local_temporal_corruption_restoration", "name": "Robot Manipulation Local Temporal Corruption Restoration", "modality": "tv2v", "prompt": "Restore the original chronological order of this video. Preserve the same frames, scene, and viewpoint." }, { "id": "Task_6_autonomous_driving_object_recognition", "name": "autonomous driving object recognition", "modality": "tv2v", "prompt": "This is a driving video. Produce a label-only semantic segmentation video on a black canvas. Do not copy or blend any RGB pixels from the input. Keep only task-relevant classes (vehicles, people, cyclists, lane markings where available, traffic lights/signs, and related moving objects); render all other pixels black. Intended target colour legend (Cityscapes colours + SYNTHIA lane-marking green) — car: rgb(0,0,142); traffic sign: rgb(220,220,0); pedestrian: rgb(220,20,60); bicycle: rgb(119,11,32); motorcycle: rgb(0,0,230); traffic light: rgb(250,170,30); rider: rgb(255,0,0); truck: rgb(0,0,70); bus: rgb(0,60,100); train: rgb(0,80,100); lane marking: rgb(0,175,0)." }, { "id": "Task_70_video_stabilization", "name": "video-stabilization", "modality": "tv2v", "prompt": "Stabilize the input video by removing unintended high-frequency camera shake while preserving intentional camera motion, scene content, subject motion, timing, and visual appearance. Avoid black borders, excessive cropping, geometric distortion, wobble, and temporal artifacts." }, { "id": "Task_91_sparse_frame_motion_restoration", "name": "sparse_frame_motion_restoration", "modality": "tv2v", "prompt": "The Input is a 2-second video in which approximately 40% of temporal positions across three intervals have been replaced by repeated frozen frames. Restore plausible continuous motion at those positions while preserving every original visible frame unchanged. Return a video with exactly the same duration, frame rate, frame count, resolution, and timeline as the Input." }, { "id": "Task_9_old_film_restoration", "name": "old film restoration", "modality": "tv2v", "prompt": "Remove the motion blur from this video and restore a sharp version. Preserve the exact scene content, frame order, motion, colours, textures, resolution, and framing; do not crop, recolour, add, or remove anything." } ], "models": [ "cosmos3-nano-v2v", "cosmos3-super-v2v", "hy-omniweaving-v2v", "ltx-2.3-dev-v2v", "ltx-2.3-extend-local", "minimax-h3-local", "wan-vace-14b-v2v" ], "rows": [ { "identity": "wan-vace-14b-v2v|Task_130_video_motion_deblurring__000001|000001", "model": "wan-vace-14b-v2v", "task": "Task_130_video_motion_deblurring", "task_id": "Task_130_video_motion_deblurring__000001", "domain": "tv2v", "sample_id": "000001", "quality_status": "automated_visual_integrity_passed", "quality_label": "Quality passed", "visual_protocol_integrity_passed": true, "semantic_correctness_certified": false, "public_playable": true, "selection": "quality_baseline", "objects": { "sidecar": { "key": "experiments/full170-frozen-20261013/inferences/full/wan-vace-14b-v2v/tv2v/Task_130_video_motion_deblurring__000001.json", "version_id": 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