Neural Garment Dynamics via Manifold-Aware Transformers

dc.contributor.authorLi, Peizhuoen_US
dc.contributor.authorWang, Tuanfeng Y.en_US
dc.contributor.authorKesdogan, Timur Leventen_US
dc.contributor.authorCeylan, Duyguen_US
dc.contributor.authorSorkine-Hornung, Olgaen_US
dc.contributor.editorBermano, Amit H.en_US
dc.contributor.editorKalogerakis, Evangelosen_US
dc.date.accessioned2024-04-30T09:07:56Z
dc.date.available2024-04-30T09:07:56Z
dc.date.issued2024
dc.description.abstractData driven and learning based solutions for modeling dynamic garments have significantly advanced, especially in the context of digital humans. However, existing approaches often focus on modeling garments with respect to a fixed parametric human body model and are limited to garment geometries that were seen during training. In this work, we take a different approach and model the dynamics of a garment by exploiting its local interactions with the underlying human body. Specifically, as the body moves, we detect local garment-body collisions, which drive the deformation of the garment. At the core of our approach is a mesh-agnostic garment representation and a manifold-aware transformer network design, which together enable our method to generalize to unseen garment and body geometries. We evaluate our approach on a wide variety of garment types and motion sequences and provide competitive qualitative and quantitative results with respect to the state of the art.en_US
dc.description.number2
dc.description.sectionheadersCloth Simulation
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume43
dc.identifier.doi10.1111/cgf.15028
dc.identifier.issn1467-8659
dc.identifier.pages11 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.15028
dc.identifier.urihttps://diglib.eg.org/handle/10.1111/cgf15028
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleNeural Garment Dynamics via Manifold-Aware Transformersen_US
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