The ability to grasp objects, signal with gestures, and share emotion through touch all stem from the unique capabilities of human hands. Yet creating high-quality personalized hand avatars from images remains challenging due to complex geometry, appearance, and articulation, particularly under unconstrained lighting and limited views. Progress has also been limited by the lack of datasets that jointly provide accurate 3D geometry, high-resolution multiview imagery, and a diverse population of subjects. To address this, we present PALM, a large-scale dataset comprising 13k high-quality hand scans from 263 subjects and 90k multi-view images, capturing rich variation in skin tone, age, and geometry. To show its utility, we present a baseline PALM-Net, a multi-subject prior over hand geometry and material properties learned via physically based inverse rendering, enabling realistic, relightable single-image hand avatar personalization. PALM's scale and diversity make it a valuable real-world resource for hand modeling and related research.

翻译：人类抓握物体、手势交流以及通过触觉传递情感的能力，均源于手部独特的生理结构。然而，由于手部复杂的几何形态、外观表现及关节活动特性，尤其在非约束光照和有限视角条件下，从图像中创建高质量个性化手部化身仍具挑战性。现有研究进展同时受限于缺乏能同步提供精确三维几何、高分辨率多视角图像及多样化受试者群体的数据集。为此，我们提出PALM——一个包含263名受试者共1.3万次高质量手部扫描与9万张多视角图像的大规模数据集，其完整记录了肤色、年龄及几何形态的丰富变异。为验证其实用性，我们构建基线模型PALM-Net，该模型通过基于物理的逆向渲染学习手部几何与材质属性的多主体先验知识，可实现真实感强、可重照明的单图像手部化身个性化定制。PALM数据集的规模与多样性使其成为手部建模及相关研究领域极具价值的现实世界资源。