VGGRPO: Towards World-Consistent Video Generation with 4D Latent Reward

Large-scale video diffusion models achieve impressive visualquality, yet often fail to preserve geometric consistency. Prior approachesimprove consistency either by augmenting the generator with additionalmodules or applying geometry-aware alignment. However, architecturalmodifications can compromise the generalization of internet-scale pre-trained models, while existing alignment methods are limited to staticscenes and rely on RGB-space rewards that require repeated VAE de-coding, incurring substantial compute overhead and failing to general-ize to highly dynamic real-world scenes. To preserve the pretrained ca-pacity while improving geometric consistency, we propose VGGRPO(Visual Geometry GRPO), a latent geometry-guided framework forgeometry-aware video post-training. VGGRPO introduces a Latent Ge-ometry Model (LGM) that stitches video diffusion latents to geometryfoundation models, enabling direct decoding of scene geometry from thelatent space. By constructing LGM from a geometry model with 4D re-construction capability, VGGRPO naturally extends to dynamic scenes,overcoming the static-scene limitations of prior methods. Building onthis, we perform latent-space Group Relative Policy Optimization withtwo complementary rewards: a camera motion smoothness reward thatpenalizes jittery trajectories, and a geometry reprojection consistencyreward that enforces cross-view geometric coherence. Experiments onboth static and dynamic benchmarks show that VGGRPO improvescamera stability, geometry consistency, and overall quality while elim-inating costly VAE decoding, making latent-space geometry-guided re-inforcement an efficient and flexible approach to world-consistent videogeneration. Our project page is here.