Geo-Align: Video Generation Alignment via Metric Geometry Reward
Summary
Geo-Align presents a reinforcement learning framework for camera-controlled video re-rendering that improves generalization through scale-aware perceptual rewards and metric 3D estimation for camera trajectory extraction.
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Paper page - Geo-Align: Video Generation Alignment via Metric Geometry Reward
Source: https://huggingface.co/papers/2605.23903
Abstract
Geo-Align presents a reinforcement learning framework for camera-controlled video re-rendering that improves generalization through scale-aware perceptual rewards and metric 3D estimation for camera trajectory extraction.
Camera-controlled video generation has achieved remarkable progress in recent years. However, existing video-to-video re-rendering methods primarily rely onSupervised Fine-Tuningusingsynthetic datasets. At present, there is an extreme scarcity of synchronized, multi-viewreal-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 andcamera trajectories. To bridge this gap, we propose Geo-Align, the firstReinforcement Learningframework specifically designed forcamera-controlled video re-rendering. Built upon apretrained model, we optimize the model through ascale-aware perceptual rewardmechanism. Specifically, we introduce ametric 3D estimatorto extract precisecamera trajectoriesfrom generated videos, explicitly penalizing deviations in rotation and translation. Furthermore, we meticulously designed adata pipeline strategybased on real-world conditioning videos and targetcamera trajectoriesderived 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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