Human101: Training 100+FPS Human Gaussians in 100s from 1 View
Mingwei Li Jiachen Tao Zongxin Yang Yi Yang
Reconstructing the human body from single-view videos plays a pivotal role in
the virtual reality domain. One prevalent application scenario necessitates the
rapid reconstruction of high-fidelity 3D digital humans while simultaneously
ensuring real-time rendering and interaction. Existing methods often struggle
to fulfill both requirements. In this paper, we introduce Human101, a novel
framework adept at producing high-fidelity dynamic 3D human reconstructions
from 1-view videos by training 3D Gaussians in 100 seconds and rendering in
100+ FPS. Our method leverages the strengths of 3D Gaussian Splatting, which
provides an explicit and efficient representation of 3D humans. Standing apart
from prior NeRF-based pipelines, Human101 ingeniously applies a Human-centric
Forward Gaussian Animation method to deform the parameters of 3D Gaussians,
thereby enhancing rendering speed (i.e., rendering 1024-resolution images at an
impressive 60+ FPS and rendering 512-resolution images at 100+ FPS).
Experimental results indicate that our approach substantially eclipses current
methods, clocking up to a 10 times surge in frames per second and delivering
comparable or superior rendering quality. Code and demos will be released at
https://github.com/longxiang-ai/Human101.