--- task_categories: - other --- # 4DCodeBench-RealWorld This dataset is part of [4DCodeBench](https://4dcodebench.com), a benchmark for evaluating coding agents on inverse graphics of dynamic scenes. It contains 100 real-world cases, each with a video of a physical event and a hand-annotated mask of the pixels that move in it. 23 of the videos cannot be redistributed; one script rebuilds them from their public sources. **Resources:** - Paper: [4DCodeBench: Benchmarking Agents on Inverse Graphics of Dynamic Scenes](https://huggingface.co/papers/2610.03715) - Project page: [https://4dcodebench.com](https://4dcodebench.com) - Code: [https://github.com/4DCodeBench/4DCodeBench](https://github.com/4DCodeBench/4DCodeBench) ## Layout ```text videos/.mp4 the reference video, as the benchmark reads it annotations//dynamic_mask.npz the dynamic mask metadata.jsonl one row per case scripts/ prepare_videos.py and what it needs ``` 135 MB of video and 95 MB of masks. Every video has at most 300 frames, at most 30 fps and a long side of at most 1280 px. `dynamic_mask.npz` holds `mask`, uint8 (frames, height, width) with 1 on a moving pixel, at the video's frame count and resolution. ## Completing the videos ```bash pip install numpy opencv-python # plus ffmpeg and ffprobe on PATH python scripts/prepare_videos.py ``` The script downloads the 23 sources into `downloads/` and writes `videos/.mp4` with the same steps that made the benchmark. Frame count, frame rate and resolution match exactly; pixels can differ slightly with the H.264 encoder. The benchmark's `download_data.py` runs this step itself. ## Metadata Each row of `metadata.jsonl` has `case`, `dataset`, `availability` (`included`, or `script` for the 23 rebuilt videos), `source`, `width`, `height`, `fps`, `frames`, `categories` (the materials involved) and `description` (the physical event). `source` is the URL of a web video and the file path inside its dataset otherwise; YouTube rows also carry `channel`, `title` and `license`. ## Sources | dataset | videos | link | |---|---|---| | WISA-80K | 33 | https://github.com/360CVGroup/WISA | | Web (Pexels, Mixkit, Pixabay, YouTube) | 28 | per video in `metadata.jsonl` | | Physics-IQ | 12 | https://github.com/google-deepmind/physics-IQ-benchmark | | ABC-130K | 5 | https://huggingface.co/datasets/XDOF/ABC-130k | | T-REX | 4 | https://huggingface.co/datasets/zekaiwang/trex_dataset | | Phys101 | 3 | http://phys101.csail.mit.edu/ | | Phys-AD | 3 | https://guyao2023.github.io/Phys-AD/ | | Robo360 | 3 | https://arxiv.org/abs/2312.06686 | | AgiBot World | 2 | https://huggingface.co/datasets/agibot-world/AgiBotWorld-Alpha | | RoboCook | 2 | https://hshi74.github.io/robocook/ | | RoboCraft | 2 | http://hxu.rocks/robocraft/ | | ALOHA Unleashed | 1 | https://aloha-unleashed.github.io/ | | ManipArena | 1 | https://github.com/maniparena/maniparena-repo | | TaskLevel-ILC | 1 | https://www.youtube.com/watch?v=FLiILOyQQbw | The five web videos from YouTube are used under CC BY, from the channels Morten Møller, Philip Yecko, ASMR City and Madly Satisfying.