AniPixel：面向可动画的像素对齐人体化身
AniPixel: Towards Animatable Pixel-Aligned Human Avatar
AI总结：
AniPixel提出一种利用像素对齐特征和双向神经蒙皮场的可动画、可泛化人体化身重建方法，实现新视角渲染和新姿态动画的先进性能。
摘要：
尽管人体重建通常产生特定于人体的化身，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，可以产生具有泛化性但可动画性受限的体积化身，因为渲染仅对静态表示可行。在本文中，我们提出AniPixel，一种新颖的可动画且可泛化的人体化身重建方法，该方法利用像素对齐特征进行身体几何预测和RGB颜色混合。在技术上，为了将规范空间与目标空间及观察空间对齐，我们提出一种基于骨骼驱动变形的双向神经蒙皮场，以建立目标到规范以及规范到观察的对应关系。然后，我们将规范身体几何解耦为归一化的中性尺寸身体和特定于主体的残差，以提高泛化性。由于几何和外观密切相关，我们引入像素对齐特征以促进身体几何预测，并利用详细表面法线以增强RGB颜色混合。我们还设计了一个姿态相关和视角相关的着色模块，以表示局部光照变化。实验表明，AniPixel在渲染可比的新视角的同时，比最先进的方法提供更好的新姿态动画结果。
英文摘要：
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.
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