基于体型建模人体自接触姿态，提升姿势估计准确性
Generative Modeling of Shape-Dependent Self-Contact Human Poses
- 用体形参数条件化潜变量扩散模型生成自接触姿态
- 在38.3万组姿态上验证，体型影响自接触分布
- 适合做单视角人体姿态估计与自接触建模的研究者
人体自接触行为高度依赖身体形态，例如低体质量指数（BMI）者揉肚子不会穿入腹部，而高BMI者则可能穿透。现有自接触数据集缺乏姿态多样性与精确体形信息，难以分析自接触与体形的关系。为此，我们构建了首个大规模自接触数据集Goliath-SC，包含130名受试者的38.3万组自接触姿态，并精确注册体形。基于此，提出一种体部位潜变量扩散模型，以体形参数为条件生成自接触先验。进一步将该先验融入单视角人体姿态估计，优化估计结果使其满足自接触约束。实验表明，体形条件对自接触分布建模至关重要，显著提升自接触场景下的姿态估计性能。
原文摘要 · Abstract (English)
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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