WBench Submission Packaging

Assemble a submission bundle and (optionally) push it to a HuggingFace dataset. Full spec: docs/SUBMISSION.md.

Bundle layout

<model_name>/
├── meta.json                    # required
├── report.json                  # required for path A (self-eval), omit for path B
├── turns.json                   # optional but recommended (non-uniform turns)
└── videos/
    └── case_<id>_combined.mp4

<id> is the case's JSON id field (e.g. 1, e_5, ps_3), never the filename.

Two submission paths

• A — self-evaluation (default): include report.json (run the wbench-evaluate skill first). We re-run a random subset to confirm reproducibility.
• B — we evaluate (fallback): omit report.json, submit videos only; we run the 22 metrics. Batched.

Required case coverage

1. meta.json

{
  "model_name": "mymodel",
  "type": "text",                 // text | camera | action
  "display_name": "My Model 1.0",
  "org": "My Lab",
  "sampled_by": "...",            // who generated the videos
  "contact": "you@example.com",
  "num_videos": 289,
  "split": "full"                 // full | navi
}

2. turns.json (recommended)

Per-turn metrics need per-turn frame ranges. Models allot a non-uniform number of frames per turn, so provide the boundaries explicitly:

{
  "fps": 24,
  "cases": {
    "1":   { "turn_frames": [0, 57, 97, 137, 177] },
    "e_5": { "turn_frames": [0, 60, 120, 180] }
  }
}

• turn_frames = start frame of each turn plus a final sentinel; turn i spans [turn_frames[i], turn_frames[i+1]).
• N turns → N+1 boundaries, strictly increasing, start at 0, end ≤ actual frame count.
• Omitting it → uniform split (total_frames / n_turns). Fine for equal-length turns (kling), but understates per-turn metrics for non-uniform turns (most autoregressive world models).

Generate programmatically from your model's frames-per-turn rule + the per-turn chunk_length exposed by case_to_poses / case_to_actions in src/models/{camera,action}. Validate before shipping:

import cv2, json
turns = json.load(open("work_dirs/<model>/turns.json"))
for cid, t in turns["cases"].items():
    tf = t["turn_frames"]
    assert tf[0] == 0 and tf == sorted(tf) and len(set(tf)) == len(tf), cid
    nf = int(cv2.VideoCapture(f"work_dirs/<model>/videos/case_{cid}_combined.mp4").get(7))
    assert tf[-1] <= nf, f"{cid}: sentinel {tf[-1]} > {nf} frames"
print("turns.json OK")

3. Upload to HuggingFace (private dataset)

The reference bundles live under meituan-longcat/WBench-examples (folders hyworld1.5/, kling3/). If your network needs a proxy to reach huggingface.co, export https_proxy / http_proxy first.

# export https_proxy=http://<your-proxy>:<port> http_proxy=http://<your-proxy>:<port>
export HF_TOKEN=<write-scoped token>     # from https://huggingface.co/settings/tokens

REPO=meituan-longcat/WBench-examples

# create once (skip if it exists)
hf repo create "$REPO" --repo-type dataset --private

# upload a bundle (local dir → repo subfolder). Resumable: re-run to retry.
hf upload "$REPO" work_dirs/<model> <model> --repo-type dataset

Large bundles (multi-GB) — run in the background and watch the log:

mkdir -p logs
nohup bash -c 'export HF_TOKEN=<token>; \
  hf upload meituan-longcat/WBench-examples work_dirs/<model> <model> --repo-type dataset' \
  > logs/hf_upload_<model>.log 2>&1 &

Gotchas

• meta.json model_name must match the bundle/repo folder name (e.g. kling3, hyworld1.5).
• mp4 files are auto-tracked as LFS by hf upload — no manual git lfs track.
• hf upload is resumable: an interrupted upload re-runs and skips files already pushed.
• A HuggingFace dataset link with this exact structure can be submitted instead of raw files (videos are large).
• docs/SUBMISSION.md mentions a validate_submission.py — it is not yet in the repo; validate turns.json with the snippet above for now.