Datasets:
Point to CodeGoat24/WorldReward-9B
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README.md
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@@ -24,9 +24,14 @@ A human-annotated preference benchmark for **camera-conditioned world models**.
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source image and the *same* camera-action sequence, with human verdicts on three
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independent axes.
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Companion to [`CodeGoat24/WorldReward`](https://huggingface.co/CodeGoat24/WorldReward). Code: https://github.com/CodeGoat24/WorldReward
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##
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| Axis | Question |
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|---|---|
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videos/<pair_id>/right_overlay.mp4 # right.mp4 with the commanded action burned in
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```
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The `*_overlay.mp4` files are a visualisation aid for inspecting trajectories by
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eye.
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## Schema
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| `style` | string | `photo` / `game_anime` / `traditional` |
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| `label` | object | Human verdicts: `action`, `appearance`, `motion`, each `left`/`right`/`tie` |
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## Composition
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Trajectory groups:
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| `traditional` | 61 | 8.0% |
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Videos come from 9 generation systems compared pairwise. Ground-truth
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verdicts are close to balanced between the two positions
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| Axis | left | right | tie |
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|---|---:|---:|---:|
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| 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 |
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| **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_ |
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## Usage
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python scripts/run_inference.py \
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--render-root outputs/rendered_chunks \
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--output outputs/chunk_predictions.json \
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--model CodeGoat24/WorldReward
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python worldreward-bench/score.py \
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--bench data/WorldReward-Bench/bench.jsonl \
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--predictions outputs/chunk_predictions.json
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source image and the *same* camera-action sequence, with human verdicts on three
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independent axes.
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Companion to [`CodeGoat24/WorldReward-9B`](https://huggingface.co/CodeGoat24/WorldReward-9B). Code: https://github.com/CodeGoat24/WorldReward
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## What makes this benchmark different
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Most video-generation benchmarks score a single video for overall quality. Here
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each item is a **head-to-head comparison** under a *known camera trajectory*, and
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the three axes are annotated separately, so a model that moves the camera
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correctly but renders it badly is distinguishable from the reverse:
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| Axis | Question |
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|---|---|
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videos/<pair_id>/right_overlay.mp4 # right.mp4 with the commanded action burned in
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```
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`pair_id` runs from `wrb_0001` to `wrb_0760`. The ids are opaque and carry no
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meaning: consecutive ids are unrelated, and the numbering encodes nothing about
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trajectory, style, or which systems were compared. Use `trajectory_group`, `style`
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and `left`/`right` `model` for those.
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The `*_overlay.mp4` files are a visualisation aid for inspecting trajectories by
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eye. They are **not** model input; evaluation reads only `left.mp4` / `right.mp4`.
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Source images keep their original format and pixels when they are at most
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2048px on the long side. Larger ones are downscaled to 2048px and saved
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as JPEG, matching what the evaluation pipeline feeds the model anyway. Always
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read the path from `bench.jsonl` rather than assuming an extension.
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## Schema
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| `style` | string | `photo` / `game_anime` / `traditional` |
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| `label` | object | Human verdicts: `action`, `appearance`, `motion`, each `left`/`right`/`tie` |
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Action tokens combine at most one translation (`forward`, `backward`, `left`,
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`right`, `forward_left`, ...) with at most one rotation (`camera_up` = pitch up,
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`camera_down` = pitch down, `camera_left` = yaw left, `camera_right` = yaw right,
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plus `camera_ul`/`ur`/`dl`/`dr` diagonals), joined by `+`.
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## Composition
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Trajectory groups:
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| `traditional` | 61 | 8.0% |
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Videos come from 9 generation systems compared pairwise. Ground-truth
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verdicts are close to balanced between the two positions, so a predictor that
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always answers `left` scores near chance:
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| Axis | left | right | tie |
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|---|---:|---:|---:|
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| 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 |
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| **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_ |
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`UnifiedReward-Think` / `-Flex` returned nothing for 2 and 28 pairs, excluded from
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their denominators (758 / 732). Every other predictor covers all 760.
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The aggregation rule behind these numbers, and how to reproduce them, are in
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https://github.com/CodeGoat24/WorldReward.
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## Usage
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python scripts/run_inference.py \
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--render-root outputs/rendered_chunks \
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--output outputs/chunk_predictions.json \
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--model CodeGoat24/WorldReward-9B
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python worldreward-bench/score.py \
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--bench data/WorldReward-Bench/bench.jsonl \
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--predictions outputs/chunk_predictions.json
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