Topo4D: Topology-Preserving Gaussian Splatting for High-Fidelity 4D Head Capture

Xuanchen Li, Yuhao Cheng, Xingyu Ren, Haozhe Jia, Di Xu, Wenhan Zhu, Yichao Yan* ;

"Recent significant advances in high-quality face reconstruction have been made, but challenges remain in 4D face asset reconstruction. 4D head capture aims to generate dynamic topological meshes and corresponding texture maps from videos, which is widely utilized in movies and games for its ability to simulate facial muscle movements and recover dynamic textures in pore-squeezing. The industry often adopts a method involving multi-view stereo and non-rigid alignment. However, this approach is prone to errors and heavily relies on time-consuming manual processing by artists. To simplify this process, we propose Topo4D, a novel framework for automatic geometry and texture generation that optimizes densely aligned 4D heads and 8K texture maps directly from calibrated multi-view time-series images. Specifically, we first represent the time-series faces as a set of dynamic 3D Gaussians with fixed topology in which the Gaussian centers are bound to the mesh vertices. Afterward, we optimize geometry and texture frame-by-frame alternatively for dynamic head capture while maintaining temporal topology stability. Finally, we can extract dynamic facial meshes in regular wiring arrangement and high-fidelity textures with pore-level details from the learned Gaussians. Extensive experiments show that our method achieves superior results than the current SOTA face reconstruction methods in the quality of both meshes and textures. Project page: https://xuanchenli.github.io/Topo4D/."