Zimeng Zhao, Ruting Rao and Yangang Wang

Embedding a unified skeleton into diverse hand meshes is a prominent task both for animation and pose estimation. Most existing methods extracted skeletons from humanoid characters under simple poses, for example T-pose or A-pose. Applying them directly to hand meshes may yield inaccurate or implausible results because hands have higher dexterity and similar endpoints. Furthermore, these methods did not attempt to extract skeleton directly from a scan model which may be not watertight and has much more vertices. Our key idea is to unwrap meshes with different topologies in the same image-based representation, named SUPPLE (Spherical UnwraPping ProfiLEs), and then train a convolutional encoder-decoder to extract skeleton under this representation. Experiments demonstrate that our framework produces reliable and accurate skeleton estimation results across a broad range of datasets, from raw scans to artist-designed models.