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README.md

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 ---
-license: cc-by-4.0
+license: cc-by-nc-4.0
+pretty_name: ViewBench
+tags:
+- video
+- world-models
+- camera-conditioned-video
+- view-consistency
+- loop-closure
+- ue5
+- arxiv:2602.07854
+language:
+- en
 ---
+
+# ViewBench
+
+ViewBench is a dataset for camera-conditioned long-horizon video world models. It is designed to evaluate view consistency and loop closure: when a camera returns to a previously observed viewpoint, the generated observation should preserve stable scene structure and appearance.
+
+This dataset accompanies the paper [Consistent Video World Model With Geometry-Aware Rotary Position Embedding](https://arxiv.org/abs/2602.07854).
+
+## Highlights
+
+- Complete yaw, pitch, and roll coverage for controlled camera motion.
+- Round-trip loop-closure trajectories where the camera returns to previously visited viewpoints.
+- 10 photorealistic UE5 environments spanning indoor, outdoor, urban, industrial, historical, and suburban scenes.
+- Per-frame SE(3) camera-to-world poses and depth-based geometric overlap annotations.
+
+## Dataset Contents
+
+This v1 release contains the public ViewBench training split described in the paper: 1,059 UE5-rendered video sequences, about 500k frames at 30 fps, across 10 photorealistic environments.
+
+The release is organized into two trajectory groups:
+
+- `pure_rotation`: stationary-camera rotate-away-rotate-back trajectories for loop closure.
+- `rotation_translation`: compact exploration trajectories with both rotation and translation.
+
+The original internal directories were `STAGE1` and `STAGE3`. In this public release they are renamed to `pure_rotation` and `rotation_translation`.
+
+The paper's held-out evaluation set is separately collected and is not included in this training release unless explicitly provided in a later update.
+
+## Files
+
+The dataset is distributed as `tar.zst` shards plus `manifest.json`:
+
+- `pure_rotation_0000.tar.zst` ... `pure_rotation_0011.tar.zst` (600 sequences)
+- `rotation_translation_0000.tar.zst` ... `rotation_translation_0009.tar.zst` (459 sequences)
+- `manifest.json`
+
+`manifest.json` records the shard membership, sequence IDs, original-to-public stage mapping, and archive contents.
+
+Each archive extracts into:
+
+```text
+ViewBench4Training/
+  pure_rotation/
+    frames/{sequence_id}/
+    jsons/{sequence_id}.json
+    metadata/{sequence_id}/
+  rotation_translation/
+    frames/{sequence_id}/
+    jsons/{sequence_id}.json
+    metadata/{sequence_id}/
+```
+
+## Data Format
+
+Each sequence contains:
+
+- EXR frames with RGB/depth information.
+- Per-frame camera poses in `jsons/{sequence_id}.json`.
+- Raw metadata in `metadata/{sequence_id}/tickStatus.jsonl` where available.
+- Depth-based frame overlap labels in `metadata/{sequence_id}/overlap.json` where available.
+
+Camera convention:
+
+- UE left-handed coordinates: `X=forward`, `Y=right`, `Z=up`.
+- Position is measured in centimeters.
+- Rotation is `[pitch, roll, yaw]` in degrees.
+- `c2w` is a 4x4 camera-to-world SE(3) matrix.
+- Rotation convention: `R = Rz(yaw) * Ry(pitch) * Rx(roll)`.
+
+## Usage
+
+Download from ModelScope:
+
+```bash
+modelscope download --dataset JEdward/viewbench-dataset --local_dir ViewBench-v1
+```
+
+After all shards are downloaded, extract them into a single directory:
+
+```bash
+mkdir -p ViewBench4Training
+for shard in ViewBench-v1/pure_rotation_*.tar.zst ViewBench-v1/rotation_translation_*.tar.zst; do
+  tar --zstd -xf "$shard" -C ViewBench4Training
+done
+```
+
+Repeat extraction for all shards listed in `manifest.json`.
+
+## License
+
+This dataset is released for non-commercial research use under CC BY-NC 4.0-style terms.
+
+Users may use, copy, and redistribute the dataset for academic and non-commercial research purposes, provided that they give appropriate attribution and cite the accompanying paper.
+
+Commercial use, resale, or redistribution as part of a commercial dataset or product is not permitted without prior written permission from the authors.
+
+The dataset contains UE5-rendered outputs from third-party scene assets. The release does not include raw UE assets, source asset files, or engine content. Users are responsible for ensuring that their downstream use complies with applicable third-party asset terms.
+
+## Citation
+
+```bibtex
+@inproceedings{
+xiang2026consistent,
+title={Consistent Video World Model With Geometry-Aware Rotary Position Embedding},
+author={Chendong Xiang and Jiajun Liu and Jintao Zhang and Xiao Yang and Zhengwei Fang and Shizun Wang and Zijun Wang and Yingtian Zou and Hang Su and Jun Zhu},
+booktitle={ICLR 2026 the 2nd Workshop on World Models: Understanding, Modelling and Scaling},
+year={2026},
+url={https://openreview.net/forum?id=eXgmwOOvlR}
+}
+```