Geo-Align: Video Generation Alignment via Metric Geometry Reward

Zizun Li; Haoyu Guo; Runzhe Teng; Chunhua Shen; Tong He

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arXiv · 2605.23903

Geo-Align: Video Generation Alignment via Metric Geometry Reward

Zizun Li·Haoyu Guo·Runzhe Teng·Chunhua Shen·Tong He

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Abstract

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, we optimize the model through a scale-aware perceptual reward mechanism. Specifically, we introduce a metric 3D estimator to extract precise camera trajectories from generated videos, explicitly penalizing deviations in rotation and translation. Furthermore, we meticulously designed a data pipeline strategy based on real-world conditioning videos and target camera trajectories derived from synthetic data, eliminating the reliance on paired data. Extensive experiments demonstrate that Geo-Align consistently outperforms existing supervised learning baselines in both precise camera controllability and visual fidelity, indicating the effectiveness of our method.

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BibTeX RIS

Zizun Li, Haoyu Guo, Runzhe Teng, Chunhua Shen, Tong He. 2026-05-22. Geo-Align: Video Generation Alignment via Metric Geometry Reward. https://arxiv.org/abs/2605.23903

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