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:
this https URL

提出了一种名为 Topo4D 的新框架，通过优化校准的多视图时间序列图像来直接生成密集对齐的 4D 头部和 8K 纹理地图，从而简化多视图立体和非刚性对齐方法生成动态拓扑网格和对应纹理地图的过程。