ARS-Avatar：具有可学习环境光遮蔽的可动画且可重照明的Surfel化身

中文摘要 AI 辅助

从多视角图像创建可动画且可重照明的人类化身仍然具有挑战性，因为姿态相关的变形、材质和光照可见性在图像中固有地耦合在一起。在本文中，我们提出了ARS-Avatar，一种使用surfel表示的新方法，用于从在未知光照下捕获的多视角图像中生成高质量、可动画且可重照明的人类化身。我们首先从模板网格中提取变形先验，并利用它们作为驱动姿态之外的额外细节，以促进surfel属性的忠实估计和可动画化身的重建。为了支持重照明，我们采用延迟着色来估计BRDF材质。我们进一步引入了一种可微分的屏幕空间环境光遮蔽公式，通过有限差分实现身体部位特定遮蔽半径的基于梯度的优化，提供了一种光照可见性的高效近似，可与化身联合优化。大量实验表明，ARS-Avatar实现了高保真外观重建和基于物理的材质估计，同时在新姿态和光照下实现了逼真的动画和重照明。

英文摘要

Creating animatable and relightable human avatars from multi-view images remains challenging, as pose-dependent deformation, materials, and light visibility are tightly coupled in images. In this paper, we present ARS-Avatar, a novel method using surfel representation for high-quality, animatable, and relightable human avatars from multi-view images captured under unknown illumination. We first extract deformation priors from the template mesh and leverage them as additional guidance beyond driving poses, facilitating faithful estimation of surfel attributes. To support relighting, we employ deferred shading to estimate BRDF materials. We further introduce a differentiable screen-space ambient occlusion formulation that enables gradient-based optimization of body-part specific occlusion radii through finite differences, providing an efficient approximation of light visibility that can be jointly optimized with the avatar. Extensive experiments show that ARS-Avatar achieves competitive or improved radiance reconstruction on multiple metrics and consistently outperforms the evaluated PBR relighting baselines, while enabling realistic animation and relighting under novel poses and illuminations.

发表机构

• Nanjing University of Posts and Telecommunications（南京邮电大学）
• Macao Polytechnic University（澳门理工学院）
• Tsinghua University（清华大学）

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