用单视频重建逼真人像，通过网格嵌入高斯点提升细节真实感
RMAvatar: Photorealistic Human Avatar Reconstruction from Monocular Video Based on Rectified Mesh-embedded Gaussians
- 将高斯点嵌入网格面片，随网格运动实现低频形变控制
- 设计姿态相关校正模块，精准还原衣物等非刚性细节变形
- 在公开数据集上效果领先，适合虚拟人生成与数字内容创作
我们提出RMAvatar，一种基于单目视频的逼真人像重建方法，将高斯点渲染嵌入网格表面以学习穿衣服的人体形态。利用显式网格几何表示虚拟人体的运动与形状，通过高斯点实现隐式外观渲染。方法包含两个核心模块：高斯初始化模块与高斯校正模块。我们将高斯点嵌入三角面片，并通过网格控制其运动，确保人像低频运动和表面形变的一致性。由于传统线性骨骼绑定（LBS）难以处理复杂非刚性变形，我们设计了姿态相关的高斯校正模块，学习细微非刚性形变，显著提升人像的真实感与表现力。我们在多个公开数据集上进行了大量实验，RMAvatar在渲染质量与定量评估中均达到当前最佳水平。
原文摘要 · Abstract (English)
We introduce RMAvatar, a novel human avatar representation with Gaussian splatting embedded on mesh to learn clothed avatar from a monocular video. We utilize the explicit mesh geometry to represent motion and shape of a virtual human and implicit appearance rendering with Gaussian Splatting. Our method consists of two main modules: Gaussian initialization module and Gaussian rectification module. We embed Gaussians into triangular faces and control their motion through the mesh, which ensures low-frequency motion and surface deformation of the avatar. Due to the limitations of LBS formula, the human skeleton is hard to control complex non-rigid transformations. We then design a pose-related Gaussian rectification module to learn fine-detailed non-rigid deformations, further improving the realism and expressiveness of the avatar. We conduct extensive experiments on public datasets, RMAvatar shows state-of-the-art performance on both rendering quality and quantitative evaluations. Please see our project page at https://rm-avatar.github.io.
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