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Reconstructing the human body from single-view videos plays a pivotal role inthe virtual reality domain. One prevalent application scenario necessitates therapid reconstruction of high-fidelity 3D digital humans while simultaneouslyensuring real-time rendering and interaction. Existing methods often struggleto fulfill both requirements. In this paper, we introduce Human101, a novelframework adept at producing high-fidelity dynamic 3D human reconstructionsfrom 1-view videos by training 3D Gaussians in 100 seconds and rendering in100+ FPS. Our method leverages the strengths of 3D Gaussian Splatting, whichprovides an explicit and efficient representation of 3D humans. Standing apartfrom prior NeRF-based pipelines, Human101 ingeniously applies a Human-centricForward Gaussian Animation method to deform the parameters of 3D Gaussians,thereby enhancing rendering speed (i.e., rendering 1024-resolution images at animpressive 60+ FPS and rendering 512-resolution images at 100+ FPS).Experimental results indicate that our approach substantially eclipses currentmethods, clocking up to a 10 times surge in frames per second and deliveringcomparable or superior rendering quality. Code and demos will be released athttps://github.com/longxiang-ai/Human101.

Human101: Training 100+FPS Human Gaussians in 100s from 1 View