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DiffAvatar: Simulation-Ready Garment Optimization with Differentiable Simulation

Yifei Li · Hsiaoyu Chen · Egor Larionov · Nikolaos Sarafianos · Wojciech Matusik · Tuur Stuyck

Arch 4A-E Poster #408
[ ] [ Project Page ]
Wed 19 Jun 10:30 a.m. PDT — noon PDT


The realism of digital avatars is crucial in enabling telepresence applications with self-expression and customization. A key aspect of this realism originates from the physical accuracy of both a true-to-life body shape and clothing.While physical simulations can produce high-quality, realistic motions for clothed humans, they require precise estimation of body shape and high-quality garment assets with associated physical parameters for cloth simulations. However, manually creating these assets and calibrating their parameters is labor-intensive and requires specialized expertise. To address this gap, we propose DiffAvatar, a novel approach that performs body and garment co-optimization using differentiable simulation. By integrating physical simulation into the optimization loop and accounting for the complex non-linear behavior of cloth and its intricate interaction with the body, our framework recovers body and garment geometry and extracts important material parameters in a physically plausible way. Our experiments demonstrate that our approach generates realistic clothing and body shape that can be easily used in downstream applications.

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