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We wish to thank the anonymous reviewers for their helpful comments. We are also grateful to Gonzalo Gomez and Melania Prieto for their help in the preparation of results and pieces of the simulation library, Igor Santesteban for rendering materials, and Dan Casas for his feedback overall. This work was funded in part by the European Research Council (ERC Consolidator Grant 772738 TouchDesign) and the Spanish Ministry of Science (grant TED2021-132003B-I00 BLESIM).

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SFLSH: Shape-Dependent Soft-Flesh Avatars

Publicated to:Proceedings - Siggraph Asia 2023 Conference Papers, Sa 2023. - 2023-01-01 (), DOI: 10.1145/3610548.3618242

Authors: Ramon, Pablo; Romero, Cristian; Tapia, Javier; Otaduy, Miguel A

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Abstract

We present a multi-person soft-tissue avatar model. This model maps a body shape descriptor to heterogeneous geometric and mechanical parameters of a soft-tissue model across the body, effectively producing a shape-dependent parametric soft avatar model. The design of the model overcomes two major challenges, the potential redundancy of geometric and mechanical parameters, and the complexity to obtain abundant subject data, which together induce major risk of overfitting the resulting model. To overcome these challenges, we introduce a local shape-dependent regularization of the model. We demonstrate accurate results, on par with independent per-subject estimation, accurate interpolation within the range of body shapes of the training subjects, and good generalization to unseen body shapes. As a result, we obtain a parametric soft-flesh avatar model easy to integrate in many existing applications.

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