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

• URL: http://arxiv.org/abs/2312.15258v1
• Date: Sat, 23 Dec 2023 13:41:56 GMT
• Title: Human101: Training 100+FPS Human Gaussians in 100s from 1 View
• Authors: Mingwei Li, Jiachen Tao, Zongxin Yang, Yi Yang
• Abstract summary: We introduce Human101, a novel framework adept at producing high-fidelity dynamic 3D human reconstructions from 1-view videos. Our method leverages the strengths of 3D Gaussian Splatting, which provides an explicit and efficient representation of 3D humans. Human101 clocked up to a 10 times surge in frames per second and delivered comparable or superior rendering quality.
• Score: 35.77485300265528
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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