README.md

metadata

license: apache-2.0
task_categories:
  - image-to-video
tags:
  - controllable-video-generation
  - on-policy-distillation
  - benchmark

Dense2Sparse benchmark

Paper · Project Page · Code · Checkpoints

The complete 600-clip evaluation benchmark from our study of classifier-free guidance in on-policy distillation — 6 motion-difficulty buckets × 100, built on OpenHumanVid.

Self-contained: download this and you can run the evaluation. No OpenHumanVid download, no annotation pass, no GPU-days of preprocessing.

rgb/	600 mp4	ground-truth clips, 832×480 / 81 frames
reference_image/	600 png	frame 0 of each clip — an inference input
dwpose/	600 mp4	DWPose skeleton control
depth/	600 mp4	MiDaS dpt_hybrid depth control
scribble/	600 mp4	Informative-Drawings anime_style lineart control
_gt_keypoints/	600 npz	DWPose keypoints of the GT video — what the pose metric scores against
_gt_keypoints_val/	24 npz	DWPose keypoints for the val split, in val row order
metadata.csv	600 rows	chunk → prompt; the text conditioning the eval uses
splits/*.filelist.csv	600 / 24 / 24075	the split definitions

All three control signals are ours: OpenHumanVid ships metadata CSVs only — no media, no pose — so dwpose/ is DWPose run over its videos by us, exactly like depth/ and scribble/. All are version-sensitive: a different annotator build gives a different control signal and therefore different numbers, which is why they are pinned here rather than left to you. Use _gt_keypoints/ as shipped for the same reason — the published PCK and MPJPE are scored against exactly these files.

Quick start

huggingface-cli download --repo-type dataset \
    Cuttle-fish-my/Rethinking-CFG-OPD-Dense2Sparse-Dataset \
    --local-dir data/HQ-OpenHumanVid/chunked_832x480_81f_highmotion

Then, in the code repo, expand the file lists into the split CSVs the eval code reads and score a model:

CHUNKED=data/HQ-OpenHumanVid/chunked_832x480_81f_highmotion
python scripts/data/build_splits_from_filelist.py --data_dir "$CHUNKED" --splits test_set,val_set
python scripts/data/vace_make_union_csv.py        "$CHUNKED" test_set val_set

huggingface-cli download Cuttle-fish-my/Rethinking-CFG-OPD-ckpts \
    --include 'dense-to-sparse-video/*' --local-dir models/released
bash scripts/eval/run_eval_model.sh opd 0,1,2,3 \
    models/released/dense-to-sparse-video/pdm_unicontrol.safetensors pdm

metadata.csv carries the prompt for each of the 600 chunks. These are OpenHumanVid captions, included because build_splits_from_filelist.py reads prompts from there and inference conditions on them — the published numbers were produced with these exact strings.

The validation split

val_set.filelist.csv is drawn from the benchmark: all 24 of its chunks are also in test_set.filelist.csv, 4 per motion bucket, and its extra eval_idx column is each row's index into the test file list. Checkpoint selection during training used these 24 clips.

_gt_keypoints_val/ is the val split's own extraction, not a re-index of _gt_keypoints/. Keep the two separate: each is indexed by its own CSV's row order, so scoring val clips against the test keypoints would compare every clip to the wrong person. Use both as shipped — training reads the val set, evaluation reads the test set.

Training data is not here

Only the 600 benchmark clips ship. The training split is 24 075 chunks (~99 GB once annotated) and is not redistributed — build it from your own OpenHumanVid copy with scripts/data/prepare_training_corpus.sh in the code repo, which downloads, chunks and annotates in one resumable pass.

Provenance

• 832×480, 81 frames, non-overlapping chunks of OpenHumanVid clips (n_chunks = n_frames // 81, named <clip_id>_c<index>).
• The 600 are stratified into 6 buckets of 100 by mean per-frame pixel difference of the rendered DWPose skeleton; bucket edges [1.5, 2.0, 2.5, 3.5, 5.0, 7.0, ∞). Difficulty is measured on the control signal, so it means "how much the pose actually moves" rather than "how pretty the clip is" — the two are anti-correlated in OpenHumanVid, whose aesthetic filtering biases toward near-static clips.
• The split is fixed by the released file lists; it cannot be re-sampled from the corpus.

MANIFEST.sha256 covers every file.

Licence

The annotations, keypoints and split definitions are Apache-2.0.

rgb/, reference_image/ and the prompts in metadata.csv are derived from OpenHumanVid — 600 clips of 81 frames each plus their captions, redistributed here so the benchmark is runnable on its own. OpenHumanVid carries its own terms; if you need the corpus itself, obtain it from its authors and comply with those. If you maintain OpenHumanVid and would prefer these removed, open a discussion and we will.