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README.md

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+---
+pretty_name: Camera Motion Dataset and Benchmark
+tags:
+- video
+- webdataset
+- benchmark
+- camera-motion
+---
+
+# Camera Motion Dataset and Benchmark
+
+This repository packages the dataset and benchmark release for our paper "Geometry-Guided Camera Motion Understanding in VideoLLMs" as a Hugging Face dataset repo.
+
+It contains:
+
+- WebDataset tar shards for `train`, `val`, and `test`
+- Split manifests (`train.json`, `val.json`, `test.json`) that map source clips to sample ids
+- A multiple-choice VQA benchmark (`cam-motion.jsonl`)
+- Small example scripts under `examples/`
+
+## Files
+
+| Path | Description |
+| --- | --- |
+| `train-*.tar` | Training split WebDataset shards |
+| `val-*.tar` | Validation split WebDataset shards |
+| `test-*.tar` | Test split WebDataset shards |
+| `train.json` | Training manifest with `video_path`, `clip_index`, `label`, and `id` |
+| `val.json` | Validation manifest with the same schema |
+| `test.json` | Test manifest with the same schema |
+| `cam-motion.jsonl` | Multiple-choice benchmark for camera-motion understanding |
+| `examples/load_webdataset.py` | Minimal WebDataset loader example |
+| `examples/inspect_tar.py` | Prints the internal structure of one tar shard |
+| `examples/read_benchmark.py` | Reads benchmark rows and maps them back to manifests |
+
+## Dataset Format
+
+Each WebDataset sample stores 8 RGB frames resized to `560 x 560` and a JSON metadata record:
+
+```text
+<sample_id>.jpg-00
+<sample_id>.jpg-01
+...
+<sample_id>.jpg-07
+<sample_id>.json
+```
+
+The JSON sidecar has the form:
+
+```json
+{
+  "label": "dolly in, truck right",
+  "id": "00ae5c50b2b54704ad833996c20c055d"
+}
+```
+
+The manifests provide the lookup back to source provenance:
+
+```json
+{
+  "video_path": "path/to/source/video.mp4",
+  "clip_index": 2,
+  "label": "dolly in, truck right",
+  "id": "00ae5c50b2b54704ad833996c20c055d"
+}
+```
+
+`cam-motion.jsonl` contains multiple-choice benchmark rows like:
+
+```json
+{
+  "video": "path/to/source/video.mp4",
+  "clip_index": 2,
+  "conversations": [
+    {
+      "from": "human",
+      "value": "<video>\nIdentify the camera motion depicted in the video using standard cinematographic terminology.\nOptions:\n(A) ...\n(B) ...\n(C) ...\n(D) ..."
+    },
+    {
+      "from": "gpt",
+      "value": "Answer: (A) ..."
+    }
+  ]
+}
+```
+
+The `video` field in the benchmark is a provenance string copied from dataset construction. After release, it should be matched against `video_path` in the manifest files rather than treated as a valid local path.
+
+## Current Split Layout
+
+- Manifest rows: `train=9819`, `val=1227`, `test=1228` (12274 total)
+- Materialized shard samples: matches manifests exactly (0 missing)
+- Benchmark rows: `12274` (one row per unique clip)
+
+All splits are deduplicated and non-overlapping.
+
+The example and validation scripts in this repo show how to inspect these counts locally before publishing.
+
+## Usage
+
+Download the dataset repo locally, then point `webdataset` at the shard files:
+
+```python
+import io
+import json
+from pathlib import Path
+
+from PIL import Image
+import webdataset as wds
+
+dataset_root = Path("camera-motion-dataset-and-benchmark")
+shards = sorted(str(p) for p in dataset_root.glob("train-*.tar"))
+
+def decode_sample(sample):
+    frames = [
+        Image.open(io.BytesIO(sample[f"jpg-{i:02d}"])).convert("RGB")
+        for i in range(8)
+    ]
+    meta = json.loads(sample["json"])
+    return {
+        "id": meta["id"],
+        "label": meta["label"],
+        "frames": frames,
+    }
+
+dataset = wds.WebDataset(shards, shardshuffle=False).map(decode_sample)
+sample = next(iter(dataset))
+print(sample["id"], sample["label"], len(sample["frames"]))
+```
+
+To inspect the benchmark:
+
+```bash
+python examples/read_benchmark.py --dataset-root . --show 3
+```
+
+To inspect one tar shard:
+
+```bash
+python examples/inspect_tar.py --tar train-000000.tar
+```
+
+## Suggested Hugging Face Workflow
+
+1. Stage a clean dataset repo from the source project using the local helper scripts.
+2. If you are rebuilding corrected manifests first, point `stage_release.py` at that dataset directory with `--dataset-dir`.
+3. Create a private dataset repo first.
+4. Upload the staged folder.
+5. Inspect the dataset card, files, and example scripts on the Hub.
+6. Make the repo public only after the release audit looks correct.
+
+## Citation
+
+If you publish this dataset, replace this section with your project citation.

examples/inspect_tar.py

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+import argparse
+import io
+import json
+import tarfile
+from pathlib import Path
+
+from PIL import Image
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Inspect one Camera Motion tar shard.")
+    parser.add_argument("--tar", type=Path, required=True, help="Path to a tar shard.")
+    parser.add_argument("--limit", type=int, default=2, help="Number of sample ids to inspect.")
+    return parser.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+
+    with tarfile.open(args.tar, "r") as tar:
+        members = tar.getnames()
+        sample_ids = []
+        for name in members:
+            if name.endswith(".json"):
+                sample_ids.append(name.rsplit(".", 1)[0])
+            if len(sample_ids) >= args.limit:
+                break
+
+        print(json.dumps({"tar": str(args.tar), "num_members": len(members), "sample_ids": sample_ids}, indent=2))
+
+        for sample_id in sample_ids:
+            metadata = json.loads(tar.extractfile(f"{sample_id}.json").read().decode("utf-8"))
+            frame_members = [f"{sample_id}.jpg-{i:02d}" for i in range(8)]
+            first_frame = Image.open(io.BytesIO(tar.extractfile(frame_members[0]).read()))
+            print(
+                json.dumps(
+                    {
+                        "sample_id": sample_id,
+                        "frame_members": frame_members,
+                        "metadata": metadata,
+                        "first_frame_size": first_frame.size,
+                        "first_frame_mode": first_frame.mode,
+                    },
+                    indent=2,
+                )
+            )
+
+
+if __name__ == "__main__":
+    main()

examples/load_webdataset.py

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+import argparse
+import io
+import json
+from pathlib import Path
+
+from PIL import Image
+import webdataset as wds
+
+
+def decode_sample(sample: dict) -> dict:
+    frames = [
+        Image.open(io.BytesIO(sample[f"jpg-{i:02d}"])).convert("RGB")
+        for i in range(8)
+    ]
+    metadata = json.loads(sample["json"])
+    return {
+        "id": metadata["id"],
+        "label": metadata["label"],
+        "frames": frames,
+        "__key__": sample["__key__"],
+    }
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Load Camera Motion WebDataset shards.")
+    parser.add_argument("--dataset-root", type=Path, required=True, help="Folder containing the staged HF dataset files.")
+    parser.add_argument("--split", choices=["train", "val", "test"], default="train")
+    parser.add_argument("--limit", type=int, default=3, help="Number of samples to print.")
+    return parser.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+    shards = sorted(str(path) for path in args.dataset_root.glob(f"{args.split}-*.tar"))
+    if not shards:
+        raise SystemExit(f"No shards found for split {args.split} in {args.dataset_root}")
+
+    dataset = wds.WebDataset(shards, shardshuffle=False).map(decode_sample)
+    for index, sample in enumerate(dataset):
+        print(
+            json.dumps(
+                {
+                    "index": index,
+                    "id": sample["id"],
+                    "label": sample["label"],
+                    "num_frames": len(sample["frames"]),
+                    "frame_size": sample["frames"][0].size,
+                    "webdataset_key": sample["__key__"],
+                },
+                indent=2,
+            )
+        )
+        if index + 1 >= args.limit:
+            break
+
+
+if __name__ == "__main__":
+    main()

examples/read_benchmark.py

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+import argparse
+import json
+from collections import defaultdict
+from pathlib import Path
+
+
+def build_manifest_lookup(dataset_root: Path) -> dict[tuple[str, int], list[dict]]:
+    lookup: dict[tuple[str, int], list[dict]] = defaultdict(list)
+    for split in ["train", "val", "test"]:
+        with open(dataset_root / f"{split}.json", "r") as f:
+            rows = json.load(f)
+        for row in rows:
+            lookup[(row["video_path"], row["clip_index"])].append(
+                {
+                    "split": split,
+                    "id": row["id"],
+                    "label": row["label"],
+                }
+            )
+    return lookup
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Inspect Camera Motion benchmark rows.")
+    parser.add_argument("--dataset-root", type=Path, required=True, help="Folder containing the staged HF dataset files.")
+    parser.add_argument("--show", type=int, default=3, help="Number of benchmark rows to print.")
+    return parser.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+    benchmark_path = args.dataset_root / "cam-motion.jsonl"
+    lookup = build_manifest_lookup(args.dataset_root)
+
+    with open(benchmark_path, "r") as f:
+        for index, line in enumerate(f):
+            row = json.loads(line)
+            matches = lookup.get((row["video"], row["clip_index"]), [])
+            print(
+                json.dumps(
+                    {
+                        "index": index,
+                        "video": row["video"],
+                        "clip_index": row["clip_index"],
+                        "question": row["conversations"][0]["value"],
+                        "answer": row["conversations"][1]["value"],
+                        "manifest_matches": matches,
+                    },
+                    indent=2,
+                )
+            )
+            if index + 1 >= args.show:
+                break
+
+
+if __name__ == "__main__":
+    main()

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