Although human reconstruction typically results in human-specific avatars, recent 3D scene reconstruction techniques utilizing pixel-aligned features show promise in generalizing to new scenes. Applying these techniques to human avatar reconstruction can result in a volumetric avatar with generalizability but limited animatability due to rendering only being possible for static representations. In this paper, we propose AniPixel, a novel animatable and generalizable human avatar reconstruction method that leverages pixel-aligned features for body geometry prediction and RGB color blending. Technically, to align the canonical space with the target space and the observation space, we propose a bidirectional neural skinning field based on skeleton-driven deformation to establish the target-to-canonical and canonical-to-observation correspondences. Then, we disentangle the canonical body geometry into a normalized neutral-sized body and a subject-specific residual for better generalizability. As the geometry and appearance are closely related, we introduce pixel-aligned features to facilitate the body geometry prediction and detailed surface normals to reinforce the RGB color blending. We also devise a pose-dependent and view direction-related shading module to represent the local illumination variance. Experiments show that AniPixel renders comparable novel views while delivering better novel pose animation results than state-of-the-art methods.

翻译：尽管人体重建通常生成特定于人的化身，但近期利用像素对齐特征的3D场景重建技术在泛化到新场景方面展现出潜力。将这些技术应用于人体化身重建，可得到具有泛化能力但仅能渲染静态表示、缺乏可动画化性的体素化身。本文提出AniPixel——一种新颖的可动画化且可泛化的人体化身重建方法，利用像素对齐特征进行身体几何预测与RGB颜色混合。技术层面，为对齐规范空间、目标空间与观测空间，我们提出基于骨骼驱动形变的双向神经蒙皮场，建立目标到规范空间与规范到观测空间的对应关系。随后将规范身体几何解耦为归一化中性体型与主体特定残差以增强泛化性。鉴于几何与外观密切相关，我们引入像素对齐特征辅助身体几何预测，并利用细节表面法向强化RGB颜色混合。此外设计姿态依赖与视角方向相关的光照模块，表征局部光照变化。实验表明，AniPixel在生成可比的新视角的同时，相较于现有最优方法能呈现更优的新姿态动画结果。