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TailorMe: Self-Supervised Learning of an Anatomically Constrained Volumetric Human Shape Model
Stephan Wenninger, Fabian Kemper, Ulrich Schwanecke, Mario Botsch
Self-supervised volumetric body shape model fitting anatomical template (bones and soft tissue) to surface scans, enabling localized shape manipulation and fast inference.
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.
How to read this
- Category
- Method / model: anatomically constrained volumetric human shape model
- Contributions
- Registers a volumetric anatomical template of skeleton bones and soft tissue to surface scans of the CAESAR database
- Enlarges training data via physically plausible volumetric deformation transfer across the Cartesian product of skeletons and soft tissues
- Learns the TailorMe model self-supervised, enabling shape sampling, localized shape manipulation, and fast inference from scans
- Context
- Combines anatomical-template ideas from Dicko et al.'s Anatomy Transfer and Kadlecek et al.'s personalized anatomical models with the surface shape-space tradition of Loper et al.'s SMPL, swapping global PCA control for an anatomically grounded volumetric one.Builds on: Anatomy Transfer · Reconstructing Personalized Anatomical Models for Physics-based Body Animation · SMPL: A Skinned Multi-Person Linear Model
- Correctness
- Assumes the synthesized skeleton-by-soft-tissue combinations are physically plausible enough to train on; demonstrated on CAESAR-derived data with localized control and fast scan fitting, but augmented data realism and anatomical accuracy versus real subjects are the caveats to keep in mind.
- Clarity
- Clearly motivated against PCA shape models; a first pass conveys the anatomical-template idea, a second pass is needed for the registration and deformation-transfer details.
- How to read it
- Focus on the volumetric template and the deformation-transfer data augmentation that make localized control possible; a second pass on registration and the self-supervised objective is worthwhile if building or fitting body models.
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Related work
- Data-driven Modeling of Skin and Muscle Deformation 2008 / SIGGRAPH
- Efficient and Robust Skin Slide Simulation 2017 / DigiPro
- Capturing and Animating Skin Deformation in Human Motion 2006 / SIGGRAPH
- OSSO: Obtaining Skeletal Shape from Outside 2022 / CVPR
Keywords
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