GauHuman: Articulated Gaussian Splatting from Monocular Human Videos
Shoukang Hu Ziwei Liu
We present, GauHuman, a 3D human model with Gaussian Splatting for both fast
training (1 ~ 2 minutes) and real-time rendering (up to 189 FPS), compared with
existing NeRF-based implicit representation modelling frameworks demanding
hours of training and seconds of rendering per frame. Specifically, GauHuman
encodes Gaussian Splatting in the canonical space and transforms 3D Gaussians
from canonical space to posed space with linear blend skinning (LBS), in which
effective pose and LBS refinement modules are designed to learn fine details of
3D humans under negligible computational cost. Moreover, to enable fast
optimization of GauHuman, we initialize and prune 3D Gaussians with 3D human
prior, while splitting/cloning via KL divergence guidance, along with a novel
merge operation for further speeding up. Extensive experiments on ZJU_Mocap and
MonoCap datasets demonstrate that GauHuman achieves state-of-the-art
performance quantitatively and qualitatively with fast training and real-time
rendering speed. Notably, without sacrificing rendering quality, GauHuman can
fast model the 3D human performer with ~13k 3D Gaussians.