Dressing Avatars: Deep Photorealistic Appearance for Physically Simulated Clothing
Donglai Xiang, Timur Bagautdinov, Tuur Stuyck, Fabian Prada, Javier, Romero, Weipeng Xu, Shunsuke Saito, Jingfan Guo, Breannan Smith, Takaaki, Shiratori, Yaser Sheikh, Jessica Hodgins, Chenglei Wu

TL;DR
This paper presents a neural appearance model for physically simulated clothing on avatars, achieving photorealistic rendering with dynamic shadows and realistic deformations, enabling diverse, realistic, and customizable full-body avatars.
Contribution
The paper introduces a physically-inspired neural appearance network that combines real-world data with physics-based geometry for photorealistic, dynamic clothing on animatable avatars.
Findings
Produces photorealistic appearance with view-dependent effects
Handles complex clothing dynamics and deformations
Enables realistic, customizable avatars with diverse clothing options
Abstract
Despite recent progress in developing animatable full-body avatars, realistic modeling of clothing - one of the core aspects of human self-expression - remains an open challenge. State-of-the-art physical simulation methods can generate realistically behaving clothing geometry at interactive rates. Modeling photorealistic appearance, however, usually requires physically-based rendering which is too expensive for interactive applications. On the other hand, data-driven deep appearance models are capable of efficiently producing realistic appearance, but struggle at synthesizing geometry of highly dynamic clothing and handling challenging body-clothing configurations. To this end, we introduce pose-driven avatars with explicit modeling of clothing that exhibit both photorealistic appearance learned from real-world data and realistic clothing dynamics. The key idea is to introduce a neural…
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