TailorMe: Self-Supervised Learning of an Anatomically Constrained Volumetric Human Shape Model
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Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association and John Wiley & Sons Ltd.
Abstract
Human shape spaces have been extensively studied, as they are a core element of human shape and pose inference tasks. Classic methods for creating a human shape model register a surface template mesh to a database of 3D scans and use dimensionality reduction techniques, such as Principal Component Analysis, to learn a compact representation. While these shape models enable global shape modifications by correlating anthropometric measurements with the learned subspace, they only provide limited localized shape control. We instead register a volumetric anatomical template, consisting of skeleton bones and soft tissue, to the surface scans of the CAESAR database. We further enlarge our training data to the full Cartesian product of all skeletons and all soft tissues using physically plausible volumetric deformation transfer. This data is then used to learn an anatomically constrained volumetric human shape model in a self-supervised fashion. The resulting TAILORME model enables shape sampling, localized shape manipulation, and fast inference from given surface scans.
Description
CCS Concepts: Computing methodologies -> Mesh models; Volumetric models; Shape analysis; Learning latent representations
@article{10.1111:cgf.15046,
journal = {Computer Graphics Forum},
title = {{TailorMe: Self-Supervised Learning of an Anatomically Constrained Volumetric Human Shape Model}},
author = {Wenninger, Stephan and Kemper, Fabian and Schwanecke, Ulrich and Botsch, Mario},
year = {2024},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.15046}
}