  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 the method involving multi-view
stereo and non-rigid alignment. However, this approach is prone to errors and
heavily reliant on time-consuming manual processing by artists. To simplify
this process, we propose Topo4D, a novel framework for automatic geometry and
texture generation, which 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 perform alternative geometry and texture optimization
frame-by-frame for high-quality geometry and texture learning 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 both in the
quality of meshes and textures. Project page:
https://xuanchenli.github.io/Topo4D/.
