FB-4D: Spatial-Temporal Coherent Dynamic 3D Content Generation with Feature Banks
This addresses the challenge of achieving coherent dynamic 3D generation for applications in computer graphics and AI, representing an incremental improvement through a novel integration of feature storage and fusion.
The paper tackles the problem of generating high-fidelity dynamic 3D content with spatial-temporal consistency by proposing FB-4D, a framework that uses a Feature Bank mechanism to store and fuse features across frames, resulting in significant performance gains over existing methods, including surpassing multi-view tuning-free approaches by a large margin and matching training-based methods.
With the rapid advancements in diffusion models and 3D generation techniques, dynamic 3D content generation has become a crucial research area. However, achieving high-fidelity 4D (dynamic 3D) generation with strong spatial-temporal consistency remains a challenging task. Inspired by recent findings that pretrained diffusion features capture rich correspondences, we propose FB-4D, a novel 4D generation framework that integrates a Feature Bank mechanism to enhance both spatial and temporal consistency in generated frames. In FB-4D, we store features extracted from previous frames and fuse them into the process of generating subsequent frames, ensuring consistent characteristics across both time and multiple views. To ensure a compact representation, the Feature Bank is updated by a proposed dynamic merging mechanism. Leveraging this Feature Bank, we demonstrate for the first time that generating additional reference sequences through multiple autoregressive iterations can continuously improve generation performance. Experimental results show that FB-4D significantly outperforms existing methods in terms of rendering quality, spatial-temporal consistency, and robustness. It surpasses all multi-view generation tuning-free approaches by a large margin and achieves performance on par with training-based methods.