LISA: Learning Implicit Shape and Appearance of Hands
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Abstract (arXiv)
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/.
Key facts. Cited by 63 papers in the map (data through 7 Oct 2026). Builds on 61 of its 74 references that are in the map. First posted on arXiv 4 Apr 2022.
Builds on
61 of 74 references.
References · 61
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50 most cited of 61 All 61 (JSON)
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Citing papers per year: 2022: 11; 2023: 23; 2024: 16; 2025: 9; 2026: 4.
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50 most cited of 63 All 63 (JSON)
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50 newest of 63 All 63 (JSON)
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- 2025PALM: A Dataset and Baseline for Learning Multi-subject Hand Prior7 Nov 2025
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