task_categories:
- other
4DCodeBench-RealWorld
This dataset is part of 4DCodeBench, 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
- Project page: https://4dcodebench.com
- Code: https://github.com/4DCodeBench/4DCodeBench
Layout
videos/<case>.mp4 the reference video, as the benchmark reads it
annotations/<case>/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
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/<case>.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.