Dressing Avatars
نویسندگان
چکیده
Despite recent progress in developing animatable full-body avatars, realistic modeling of clothing - one the core aspects human self-expression remains an open challenge. State-of-the-art physical simulation methods can generate realistically behaving geometry at interactive rates. Modeling photorealistic appearance, however, usually requires physically-based rendering which is too expensive for applications. On other hand, data-driven deep appearance models are capable efficiently producing but struggle synthesizing highly dynamic and handling challenging body-clothing configurations. To this end, we introduce pose-driven avatars with explicit that exhibit both learned from real-world data dynamics. The key idea to a neural model operates on top geometry: training time use high-fidelity tracking, whereas animation rely physically simulated geometry. Our contribution physically-inspired network, generating view-dependent shadowing effects even unseen We conduct thorough evaluation our demonstrate diverse results several subjects different types clothing. Unlike previous work approach produce much richer dynamics more deformations many examples loose also formulation naturally allows be used people while staying fully animatable, thus enabling, first time, novel
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ژورنال
عنوان ژورنال: ACM Transactions on Graphics
سال: 2022
ISSN: ['0730-0301', '1557-7368']
DOI: https://doi.org/10.1145/3550454.3555456