We present HandAvatar, a novel representation for hand animation and rendering, which can generate smoothly compositional geometry and self-occlusion-aware texture. Specifically, we first develop a MANO-HD model as a high-resolution mesh topology to fit personalized hand shapes. Sequentially, we decompose hand geometry into per-bone rigid parts, and then re-compose paired geometry encodings to derive an across-part consistent occupancy field. As for texture modeling, we propose a self-occlusion-aware shading field (SelF). In SelF, drivable anchors are paved on the MANO-HD surface to record albedo information under a wide variety of hand poses. Moreover, directed soft occupancy is designed to describe the ray-to-surface relation, which is leveraged to generate an illumination field for the disentanglement of pose-independent albedo and pose-dependent illumination. Trained from monocular video data, our HandAvatar can perform free-pose hand animation and rendering while at the same time achieving superior appearance fidelity. We also demonstrate that HandAvatar provides a route for hand appearance editing. Project website: https://seanchenxy.github.io/HandAvatarWeb.

翻译：我们提出HandAvatar，一种用于手部动画与渲染的新型表示方法，可生成平滑组合的几何形状和自遮挡感知纹理。具体而言，我们首先开发了一个高分辨率网格拓扑模型MANO-HD，用于拟合个性化手部形状；随后将手部几何解耦为各骨骼刚体部分，再重新组合成对几何编码以推导跨部件一致的占据场。在纹理建模方面，我们提出自遮挡感知着色场（SelF）。SelF在MANO-HD表面铺设可驱动锚点，记录宽泛手部姿态下的反照率信息。此外，我们设计了定向软占据来描述射线与表面的关系，并利用其生成光照场，实现姿态无关反照率与姿态相关光照的解耦。经单目视频数据训练，HandAvatar可执行自由姿态手部动画与渲染，同时实现卓越的外观保真度。我们还证明HandAvatar为手部外观编辑提供了可行路径。项目网站：https://seanchenxy.github.io/HandAvatarWeb。