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：抓握物体、手势交流以及通过触觉传递情感的能力，均源于人类手部独特的机能。然而，由于手部复杂的几何结构、外观表现和关节活动，特别是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。此外，现有数据集普遍缺乏同时提供精确三维几何、高分辨率多视角图像以及多样化受试者群体的数据，这也限制了相关研究的进展。为此，我们提出了PALM——一个大规模数据集，包含来自263名受试者的13,000个高质量手部扫描数据和90,000张多视角图像，涵盖了肤色、年龄和几何形态的丰富变异。为展示其应用价值，我们提出了基线模型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，能够实现逼真、可重光照的单图像手部化身个性化。PALM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真实世界资源。