| --- |
| license: apache-2.0 |
| task_categories: |
| - video-classification |
| language: |
| - en |
| tags: |
| - reward-model |
| - world-model |
| - video-generation |
| - camera-control |
| - preference-data |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: default |
| data_files: bench.jsonl |
| --- |
| |
| # WorldReward-Bench |
|
|
| A human-annotated preference benchmark for **camera-conditioned world models**. |
| 760 pairs of videos, each pair generated by two different models from the *same* |
| source image and the *same* camera-action sequence, with human verdicts on three |
| independent axes. |
|
|
| - 📰 Paper: https://arxiv.org/abs/2609.03952 |
| - 🪐 Project Page: https://codegoat24.github.io/WorldReward |
| - 🤗 Model Collections: https://huggingface.co/CodeGoat24/WorldReward-9B |
| - 🚀 Github: https://github.com/CodeGoat24/WorldReward |
| - 👋 Point of Contact: [Yibin Wang](https://codegoat24.github.io) |
|
|
|  |
|
|
|
|
| ## Evaluation Dimensions |
|
|
| | Axis | Question | |
| |---|---| |
| | `action` | Did the camera actually execute the commanded motion — right direction, right magnitude, no un-commanded drift? | |
| | `appearance` | Which video looks better — fewer artifacts, more stable structure, faithful to the source scene? | |
| | `motion` | Which video is genuinely *generating* new content, rather than sliding a static texture or melting? | |
|
|
| Each verdict is `left`, `right`, or `tie`. |
|
|
| ## Contents |
|
|
| ``` |
| bench.jsonl # 760 pairs, one JSON object per line |
| videos/<pair_id>/source.* # shared source image (exact path is in bench.jsonl) |
| videos/<pair_id>/left.mp4 # the video shown on the left |
| videos/<pair_id>/right.mp4 # the video shown on the right |
| videos/<pair_id>/left_overlay.mp4 # left.mp4 with the commanded action burned in |
| videos/<pair_id>/right_overlay.mp4 # right.mp4 with the commanded action burned in |
| ``` |
|
|
|
|
| The `*_overlay.mp4` files are a visualisation aid for inspecting trajectories by |
| eye. |
|
|
| Source images keep their original format and pixels when they are at most |
| 2048px on the long side. Larger ones are downscaled to 2048px and saved |
| as JPEG. |
|
|
| ## Schema |
|
|
| | Field | Type | Description | |
| |---|---|---| |
| | `pair_id` | string | Opaque identifier, `wrb_0001`-style. Carries no metadata. | |
| | `input_image` | path | Source image both videos were generated from | |
| | `input_caption` | string | English description of the source scene | |
| | `actions` | list[string] | Commanded camera-action sequence, one token per step | |
| | `frames_per_action` | int | Frames each action occupies | |
| | `num_frames` | int | Total frames per video | |
| | `left` / `right` | object | `video`, `overlay`, and the generating `model` name | |
| | `trajectory_family` | string | One of 9 fine-grained trajectory types | |
| | `trajectory_group` | string | `pure_translation` / `pure_rotation` / `compound` | |
| | `style` | string | `photo` / `game_anime` / `traditional` | |
| | `label` | object | Human verdicts: `action`, `appearance`, `motion`, each `left`/`right`/`tie` | |
|
|
|
|
|
|
| ## Results |
|
|
| Three-way agreement with the human labels (%). All 760 pairs count; a pair whose |
| label is `tie` is correct only if the model also predicts `tie`. Act./App./Mot. = |
| action / appearance / motion. **Best** and _second-best_ per column; `--` marks an |
| axis a predictor does not model. |
|
|
| | Reward model | All<br>Act. | All<br>App. | All<br>Mot. | Translation<br>Act. | Translation<br>App. | Translation<br>Mot. | Rotation<br>Act. | Rotation<br>App. | Rotation<br>Mot. | Compound<br>Act. | Compound<br>App. | Compound<br>Mot. | Photo<br>Act. | Photo<br>App. | Photo<br>Mot. | Game/Anime<br>Act. | Game/Anime<br>App. | Game/Anime<br>Mot. | Art<br>Act. | Art<br>App. | Art<br>Mot. | |
| |:---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:| |
| | *Closed-source VLM* | | | | | | | | | | | | | | | | | | | | | | |
| | Gemini-3.1-Pro | 65.79 | _80.13_ | 60.79 | 64.73 | **82.19** | 64.38 | 62.90 | 83.87 | 57.53 | 68.79 | _75.53_ | 59.22 | 65.48 | **82.74** | 63.84 | 65.27 | 79.34 | 57.49 | 70.49 | 68.85 | 60.66 | |
| | GPT-5.5 | _74.21_ | 79.87 | _69.47_ | _72.95_ | _81.16_ | _74.66_ | _72.04_ | _84.41_ | 62.90 | _76.95_ | 75.53 | _68.44_ | _76.44_ | _81.64_ | _68.77_ | 71.56 | _79.64_ | _68.86_ | **75.41** | _70.49_ | **77.05** | |
| | *Image / video quality reward models* | | | | | | | | | | | | | | | | | | | | | | |
| | VideoAlign | -- | 61.32 | 40.13 | -- | 66.44 | 31.51 | -- | 60.22 | 45.16 | -- | 56.74 | 45.74 | -- | 61.10 | 41.10 | -- | 61.08 | 41.32 | -- | 63.93 | 27.87 | |
| | UnifiedReward-Flex | -- | 64.62 | 49.86 | -- | 63.90 | 46.57 | -- | 73.18 | 56.98 | -- | 59.78 | 48.55 | -- | 65.08 | 55.31 | -- | 63.64 | 47.02 | -- | 67.27 | 30.91 | |
| | UnifiedReward-Think | -- | 66.09 | 38.79 | -- | 65.41 | 32.88 | -- | 69.73 | 52.43 | -- | 64.41 | 35.94 | -- | 69.51 | 41.21 | -- | 64.37 | 37.43 | -- | 55.00 | 31.67 | |
| | Aesthetic | -- | 69.87 | -- | -- | 66.10 | -- | -- | 72.04 | -- | -- | 72.34 | -- | -- | 66.58 | -- | -- | 75.15 | -- | -- | 60.66 | -- | |
| | HPSv3 | -- | 73.68 | -- | -- | 74.66 | -- | -- | 76.34 | -- | -- | 70.92 | -- | -- | 73.15 | -- | -- | 75.45 | -- | -- | 67.21 | -- | |
| | *Geometry estimation models* | | | | | | | | | | | | | | | | | | | | | | |
| | DAv3 | 70.53 | -- | -- | 67.47 | -- | -- | 68.82 | -- | -- | 74.82 | -- | -- | 70.96 | -- | -- | _75.75_ | -- | -- | 39.34 | -- | -- | |
| | WorldMirror | 68.55 | -- | -- | 67.81 | -- | -- | 68.28 | -- | -- | 69.50 | -- | -- | 67.40 | -- | -- | 74.25 | -- | -- | 44.26 | -- | -- | |
| | *Backbone, zero-shot* | | | | | | | | | | | | | | | | | | | | | | |
| | Qwen3.5-VL-9B | 48.42 | 48.29 | 43.82 | 48.29 | 45.55 | 41.78 | 52.69 | 52.15 | 49.46 | 45.74 | 48.58 | 42.20 | 50.14 | 43.84 | 47.12 | 47.01 | 52.10 | 42.81 | 45.90 | 54.10 | 29.51 | |
| | Qwen3.5-VL-27B | 63.68 | 44.34 | 62.76 | 65.07 | 37.33 | 65.75 | 65.05 | 51.61 | _63.44_ | 61.35 | 46.81 | 59.22 | 64.93 | 38.36 | 66.85 | 62.87 | 51.50 | 58.68 | 60.66 | 40.98 | 60.66 | |
| | **WorldReward-9B** | **77.63** | **81.32** | **73.03** | **76.71** | 77.74 | **78.77** | **73.12** | **86.02** | **64.52** | **81.56** | **81.91** | **72.70** | **77.26** | 81.37 | **71.78** | **78.74** | **82.04** | **75.75** | _73.77_ | **77.05** | _65.57_ | |
|
|
|
|
| ## Usage |
| See https://github.com/CodeGoat24/WorldReward for the full evaluation protocol. |
|
|
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{WorldReward, |
| title={WorldReward: Reward Modeling for Camera-Conditioned World Models}, |
| author={Wang, Yibin and Wang, Zehan and Tang, Junshu and Li, Zhimin and Zhou, Yujie and Bu, Jiazi and Ling, Pengyang and Han, Feng and Zhang, Zhixiong and Xing, Long and Ding, Shengyuan and Li, Ziang and Jin, Cheng and Zang, Yuhang and Wang, Jiaqi and Pang, Tianyu}, |
| journal={arXiv preprint arXiv:2609.03952}, |
| year={2026} |
| } |
| ``` |
|
|
