Generative Modeling of Shape-Dependent Self-Contact Human Poses

• URL: http://arxiv.org/abs/2509.23393v1
• Date: Sat, 27 Sep 2025 16:26:38 GMT
• Title: Generative Modeling of Shape-Dependent Self-Contact Human Poses
• Authors: Takehiko Ohkawa, Jihyun Lee, Shunsuke Saito, Jason Saragih, Fabian Prado, Yichen Xu, Shoou-I Yu, Ryosuke Furuta, Yoichi Sato, Takaaki Shiratori,
• Abstract summary: Despite its relevance, existing self-contact datasets lack variety of self-contact poses and precise body shapes.<n>We introduce the first extensive self-contact dataset with precise body shape registration, Goliath-SC, consisting of 383K self-contact poses across 130 subjects.<n>We propose generative modeling of self-contact prior conditioned by body shape parameters, based on a body-part-wise latent diffusion with self-attention.
• Score: 48.30189394803952
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: One can hardly model self-contact of human poses without considering underlying body shapes. For example, the pose of rubbing a belly for a person with a low BMI leads to penetration of the hand into the belly for a person with a high BMI. Despite its relevance, existing self-contact datasets lack the variety of self-contact poses and precise body shapes, limiting conclusive analysis between self-contact poses and shapes. To address this, we begin by introducing the first extensive self-contact dataset with precise body shape registration, Goliath-SC, consisting of 383K self-contact poses across 130 subjects. Using this dataset, we propose generative modeling of self-contact prior conditioned by body shape parameters, based on a body-part-wise latent diffusion with self-attention. We further incorporate this prior into single-view human pose estimation while refining estimated poses to be in contact. Our experiments suggest that shape conditioning is vital to the successful modeling of self-contact pose distribution, hence improving single-view pose estimation in self-contact.

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