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.
@inproceedings{Ramon_2023_SFLSH,
author = {Ramon, Pablo and Romero, Cristian and Tapia, Javier and Otaduy, Miguel A.},
title = {SFLSH: Shape-Dependent Soft-Flesh Avatars},
booktitle = {SIGGRAPH Asia Conference Papers (SA Conference Papers ’23)},
year = {2023},
publisher = {ACM},
DOI = {10.1145/3610548.3618242}
}