Geo-Align: Video Generation Alignment via Metric Geometry Reward
Camera-controlled video generation has achieved remarkable progress in recent years. However, existing video-to-video re-rendering methods primarily rely on Supervised Fine-Tuning using synthetic datasets. At present, there is an extreme scarcity of synchronized, multi-view real-world video data. Consequently, the prevailing paradigm often exhibits limited generalization when processing out-of-distribution real-world videos, with models struggling to accurately adhere to physical scales and camera trajectories. To bridge this gap, we propose Geo-Align, the first Reinforcement Learning framework specifically designed for camera-controlled video re-rendering. Built upon a pretrained model, ...
| Authors | Zizun Li, Haoyu Guo, Runzhe Teng et al. |
|---|---|
| Primary category | cs.CV |
| Published | 22 May 2026 |
| Last updated at source | 22 May 2026 |
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