In this work, we propose a novel clothed human reconstruction method called
GaussianBody, based on 3D Gaussian Splatting. Compared with the costly neural
radiance based models, 3D Gaussian Splatting has recently demonstrated great
performance in terms of training time and rendering quality. However, applying
the static 3D Gaussian Splatting model to the dynamic human reconstruction
problem is non-trivial due to complicated non-rigid deformations and rich cloth
details. To address these challenges, our method considers explicit pose-guided
deformation to associate dynamic Gaussians across the canonical space and the
observation space, introducing a physically-based prior with regularized
transformations helps mitigate ambiguity between the two spaces. During the
training process, we further propose a pose refinement strategy to update the
pose regression for compensating the inaccurate initial estimation and a
split-with-scale mechanism to enhance the density of regressed point clouds.
The experiments validate that our method can achieve state-of-the-art
photorealistic novel-view rendering results with high-quality details for
dynamic clothed human bodies, along with explicit geometry reconstruction.

本研究提出了一种名为 GaussianBody 的新型服装人体重建方法，基于 3D 高斯散射模型。该方法通过显式姿势引导形变，引入基于物理的先验知识和正则化变换来解决动态人体重建过程中的非刚性变形和丰富的服装细节等问题。实验证明，该方法在动态穿着服装的人体重建方面能够实现最先进的逼真视角渲染效果，同时还能明确地重建几何结构。

高斯体：三维高斯板片的衣着人物重建

GaussianBody: Clothed Human Reconstruction via 3d Gaussian Splatting

We present animatable neural radiance fields (animatable NeRF) for detailed
human avatar creation from monocular videos. Our approach extends neural
radiance fields (NeRF) to the dynamic scenes with human movements via
introducing explicit pose-guided deformation while learning the scene
representation network. In particular, we estimate the human pose for each
frame and learn a constant canonical space for the detailed human template,
which enables natural shape deformation from the observation space to the
canonical space under the explicit control of the pose parameters. To
compensate for inaccurate pose estimation, we introduce the pose refinement
strategy that updates the initial pose during the learning process, which not
only helps to learn more accurate human reconstruction but also accelerates the
convergence. In experiments we show that the proposed approach achieves 1)
implicit human geometry and appearance reconstruction with high-quality
details, 2) photo-realistic rendering of the human from novel views, and 3)
animation of the human with novel poses.

本文提出了可动态变化神经辐射场 (animatable NeRF) 的方法用于从单眼视屏中创建细节丰富的人物角色，通过引入显式姿势引导变形的方式学习场景表示网络扩展神经辐射场 (NeRF) 到带有人体运动的动态场景，并展示了该方法在人物几何和外观重建、真实感渲染和动画设计等方面取得了突破性成果。