构建13000+手部扫描数据集，实现单图高保真个性化手部建模
PALM: A Dataset and Baseline for Learning Multi-subject Hand Prior
- 基于物理逆渲染学习多主体手部几何与材质先验
- 在90000张多视角图像上实现真实可光照重置的单图建模
- 覆盖263人、多种肤色与年龄，适合手部建模与数字人研究
人类用手抓握物体、比手势和通过触觉传递情感，皆源于手的独特能力。然而，从图像中创建高质量个性化手部化身仍具挑战，尤其在非约束光照和有限视角下，受限于复杂几何、外观与关节结构。进展亦因缺乏同时包含精确3D几何、高分辨率多视角图像及多样化受试者的数据集而受限。为此，我们提出PALM，一个大规模数据集，包含来自263名受试者的13,000个高质量手部扫描和90,000张多视角图像，涵盖丰富的肤色、年龄与几何差异。为展示其价值，我们提出基线模型PALM-Net，通过物理驱动的逆渲染学习多主体手部几何与材质先验，实现真实、可光照重置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的重要现实资源。
原文摘要 · Abstract (English)
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.
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