SplatGuide: Geometric Priors from 3D Gaussians for Pose-Free Novel View Synthesis
This study addresses the disconnection between geometric understanding and content generation in pose-free novel view synthesis by proposing a unified framework based on single-view 3D Gaussian Splatting. By leveraging three complementary signals—rendered images, visibility voting, and reconstruction tokens—the method provides pixel-level geometric conditioning and occlusion-aware references to effectively bridge information gaps and guide multi-view diffusion models. Extensive experiments demonstrate that this approach achieves state-of-the-art performance across multiple benchmarks. Notably, it surpasses ground-truth pose baselines on RealEstate10K, significantly enhancing both synthesis quality and geometric consistency in pose-free scenarios. These results underscore the efficacy of integrating explicit geometric priors within generative frameworks for robust novel view synthesis without camera pose supervision.