VIGOR: VIdeo Geometry-Oriented Reward for Temporal Generative Alignment

Video diffusion models lack explicit geometric supervisionduring training, leading to inconsistency artifacts such as object defor-mation, spatial drift, and depth violations in generated videos. To addressthis limitation, we propose a geometry-based reward model that leveragespretrained geometric foundation models to evaluate multi-view consis-tency through cross-frame reprojection error. Unlike previous geometricmetrics that measure inconsistency in pixel space, where pixel intensitymay introduce additional noise, our approach conducts error computa-tion in a pointwise fashion, yielding a more physically grounded and ro-bust error metric. Furthermore, we introduce a geometry-aware samplingstrategy that filters out low-texture and non-semantic regions, focusingevaluation on geometrically meaningful areas with reliable correspon-dences to improve robustness. We apply this reward model to align videodiffusion models through two complementary pathways: post-training ofa bidirectional model via SFT or Reinforcement Learning and inference-time optimization of a Causal Video Model (e.g., Streaming video genera-tor) via test-time scaling with our reward as a path verifier. Experimentalresults validate the effectiveness of our design, demonstrating that ourgeometry-based reward provides superior robustness compared to othervariants. By enabling efficient inference-time scaling, our method offersa practical solution for enhancing open-source video models without re-quiring extensive computational resources for retraining.