title: Human101: Training 100+FPS Human Gaussians in 100s from 1 View
summary: 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.
paper_authors._id: 658bb9638ff537204fb65ce0
paper_authors.name: Mingwei Li
paper_authors._id: 658bb9638ff537204fb65ce1
paper_authors.name: Jiachen Tao
paper_authors._id: 658bb9638ff537204fb65ce2
paper_authors.name: Zongxin Yang
paper_authors._id: 658bb9638ff537204fb65ce3
paper_authors.name: Yi Yang
paper_title: Human101: Training 100+FPS Human Gaussians in 100s from 1 View
paper_summary: 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.
paper_ai_summary: Human101 is a framework that reconstructs high-fidelity dynamic 3D humans from single-view videos in real-time using 3D Gaussian Splatting and Human-centric Forward Gaussian Animation, achieving high rendering speeds and quality.
paper_ai_keywords: 3D Gaussian Splatting Human-centric Forward Gaussian Animation NeRF 3D Gaussians