Researchers have introduced CamGeo, a novel framework for sparse camera-conditioned image-to-video generation that distills 3D geometric knowledge from a pre-trained video-to-3D model (VGGT) directly into the diffusion backbone. This approach uses a training-only distillation strategy and a coarse-to-fine curriculum learning to achieve 3D consistency and geometric realism without increasing inference latency. The framework addresses challenges of pose drift and motion discontinuities prevalent in existing methods that rely on dense camera poses or simple interpolation.