LISA: Learning Implicit Shape and Appearance of Hands
This work addresses the need for accurate and generalizable hand modeling in computer vision and graphics, with incremental improvements in reconstruction quality.
The paper tackles the problem of creating a comprehensive neural model for human hands that captures shape and appearance, generalizes across subjects, and reconstructs from images, achieving higher quality hand shape reconstructions compared to baseline methods.
This paper proposes a do-it-all neural model of human hands, named LISA. The model can capture accurate hand shape and appearance, generalize to arbitrary hand subjects, provide dense surface correspondences, be reconstructed from images in the wild and easily animated. We train LISA by minimizing the shape and appearance losses on a large set of multi-view RGB image sequences annotated with coarse 3D poses of the hand skeleton. For a 3D point in the hand local coordinate, our model predicts the color and the signed distance with respect to each hand bone independently, and then combines the per-bone predictions using predicted skinning weights. The shape, color and pose representations are disentangled by design, allowing to estimate or animate only selected parameters. We experimentally demonstrate that LISA can accurately reconstruct a dynamic hand from monocular or multi-view sequences, achieving a noticeably higher quality of reconstructed hand shapes compared to baseline approaches. Project page: https://www.iri.upc.edu/people/ecorona/lisa/.