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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ task_categories:
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+ - image-to-video
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+ language:
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+ - en
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+ tags:
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+ - driving
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+ - world-model
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+ - view-interpolation
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+ - drivingdojo
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+ size_categories:
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+ - 10K<n<100K
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+ ---
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+
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+ # dojo-interp-v1 — DrivingDojo densified to 24 fps
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+
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+ 10,068 five-second driving clips (1280×704, 24 fps, 73 or 121 frames) generated
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+ from DrivingDojo (Apache-2.0) 5 fps sequences with a view-interpolation video
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+ model. Every 5th generated frame is a real DrivingDojo frame; the frames in
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+ between are synthesised. Uniform keyframe spacing, guidance 2.
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+
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+ ## Layout
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+
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+ ```
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+ clips/dojo-NNN.tar tar shards, ~300 clips each, members <name>.mp4
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+ poses/dojo-poses.tar <name>.npy (T,7) float32 per-frame relative camera
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+ action [dx,dy,dz,droll,dpitch,dyaw,flag], CARLA
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+ convention, estimated with VGGT-Omega on the
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+ generated clip (unscaled: monocular)
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+ ref_actions/dojo-ref-actions.tar <clip_id>.npy actions estimated on the original
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+ 5 fps DrivingDojo frames (reference for the clip)
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+ manifest_s{0..3}.jsonl name, clip_id, frames, arc_m (null), caption, guidance
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+ qc_scores.jsonl cheap motion/sharpness QC per clip (weak tag)
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+ ```
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+
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+ `name` is `<clip_id>_w<k>`; `clip_id` is the DrivingDojo sequence id.
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+
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+ ## Generation
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+
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+ Wan2.2-TI2V-5B with a rank-128 LoRA trained with the freeze-frame keyframe
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+ recipe of the Seoul World Model (arXiv:2603.15583); 35 sampling steps, flow
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+ shift 7. Code: https://github.com/kamwoh/miniworld
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+ (`scripts/generation/interpolate_batch.py`).
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+
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+ ## Caveats
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+
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+ About 20% of DrivingDojo clips are near-stationary (stopped at lights); the QC
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+ `static` flag marks them. Generated frames can morph on close-range pans.
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+
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+ ## License and attribution
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+
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+ Source frames: DrivingDojo (Apache-2.0). This derivative keeps Apache-2.0.
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