| --- |
| language: |
| - en |
| license: cc-by-nc-4.0 |
| task_categories: |
| - video-classification |
| - visual-question-answering |
| - video-text-to-text |
| pretty_name: RoboJudge-Bench |
| tags: |
| - robojudge |
| - embodied-ai |
| - video-evaluation |
| - benchmark |
| --- |
| |
| # RoboJudge-Bench |
|
|
| **Paper:** [RoboJudge: Multimodal Language Models as Judges for Embodied Video Generation](https://huggingface.co/papers/2610.27782) |
| **Code:** [GitHub - Sphere-AI-Lab/RoboJudge](https://github.com/Sphere-AI-Lab/RoboJudge) |
| **Project page:** https://siyuanmacs.github.io/RoboJudge/ |
|
|
| RoboJudge-Bench contains 800 human-annotated embodied videos from 8 source corpora and 10 video generators. Every record includes the task instruction, locally hosted video and initial-frame URLs, PA/IA ratings, and six diagnostic sub-scores. |
|
|
| ## Files |
|
|
| - `data/test.jsonl`: benchmark metadata, instructions, links, and labels. |
| - `data/<source>/generated_data/.../*.mp4`: all 800 benchmark videos. |
| - `data/<source>/generated_data/.../prompt/`: initial frames and instructions. |
| - `metadata/summary.json`: release counts and source revision. |
|
|
| ## Scores |
|
|
| - **Physical Adherence (PA):** agent consistency, scene consistency, and interaction realism. |
| - **Instruction Alignment (IA):** agent match, object correctness, and goal completion. |
|
|
| Main scores are integers from 1 to 5. Diagnostic sub-scores are integers from 0 to 2. |
|
|
| ## Sample Usage |
|
|
| ```bash |
| python -m pip install -r requirements.txt |
| huggingface-cli download HuggingFriends/RoboJudge-Bench \ |
| --repo-type dataset --local-dir data_root |
| |
| CKPT=HuggingFriends/RoboJudge-9B \ |
| DATA_ROOT="$PWD/data_root" \ |
| LIMIT=4 \ |
| bash code/run_inference.sh |
| |
| python code/evaluate.py \ |
| outputs/predictions.jsonl \ |
| data/benchmark/robojudge_bench.jsonl |
| ``` |
