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
nonlinear 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.

使用可区分的仿真，我们提出了 DiffAvatar，一种新颖的方法，通过身体和服装的协同优化来提高数字化身的真实感，从而实现自我表达和定制；实验表明，我们的方法可以生成逼真的服装和身体形状，可轻松用于下游应用。