Test-Time Geometric Alignment for Camera-Controlled Video Generation

Camera controlled video generative models produce visually compelling results yet routinely violate the geometry of the scenes they depict. Some existing remedies inject 3D priors at training time, requiring generator-specific optimization. We instead treat geometric consistency as an inference-time alignment problem: scene observations and generated videos should depict the same underlying geometry, and embeddings from geometric foundation models provide a proxy for this agreement. We introduce GeoReward, a training-free, differentiable reward defined by geometry-embedding similarity between scene references and generated videos. The reward supports candidate search and gradient-based steering of denoising trajectories, enabling generation quality to improve as test-time compute scales. Empirically, GeoReward improves geometric consistency in both image-conditioned and video-conditioned generation across different base generators and diverse static and dynamic scenes. Video comparisons are available at https://georeward-review.github.io.