2025/12/17 by Zhao, Jinjing, Wei, Fangyun, Liu, Zhening +3
Engineering · Computer Science · #Human Motion and Animation #Generative Adversarial Networks and Image Synthesis #3D Shape Modeling and Analysis
paper · doi:10.48550/arxiv.2512.15716
Existing video generation models struggle to maintain long-term spatial and temporal consistency due to the dense, high-dimensional nature of video signals. To overcome this limitation, we propose Spatia, a spatial memory-aware video generation framework that explicitly preserves a 3D scene point cloud as persistent spatial memory. Spatia iteratively generates video clips conditioned on this spatial memory and continuously updates it through visual SLAM. This dynamic-static disentanglement design enhances spatial consistency throughout the generation process while preserving the model's ability to produce realistic dynamic entities. Furthermore, Spatia enables applications such as explicit camera control and 3D-aware interactive editing, providing a geometrically grounded framework for scalable, memory-driven video generation.