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Dressing Avatars: Deep Photorealistic Appearance for Physically Simulated Clothing

Authors:

Timur Bagautdinov,

Shunsuke Saito,

Breannan Smith,

Takaaki Shiratori,

Jessica Hodgins,

Chenglei WuAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 41, Issue 6

Article No.: 222, Pages 1 - 15

https://doi.org/10.1145/3550454.3555456

Published: 30 November 2022 Publication History

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 clothing appearance model that operates on top of explicit geometry: at training time we use high-fidelity tracking, whereas at animation time we rely on physically simulated geometry. Our core contribution is a physically-inspired appearance network, capable of generating photorealistic appearance with view-dependent and dynamic shadowing effects even for unseen body-clothing configurations. We conduct a thorough evaluation of our model and demonstrate diverse animation results on several subjects and different types of clothing. Unlike previous work on photorealistic full-body avatars, our approach can produce much richer dynamics and more realistic deformations even for many examples of loose clothing. We also demonstrate that our formulation naturally allows clothing to be used with avatars of different people while staying fully animatable, thus enabling, for the first time, photorealistic avatars with novel clothing.

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Index Terms

Dressing Avatars: Deep Photorealistic Appearance for Physically Simulated Clothing
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Computer graphics

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cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 41, Issue 6

December 2022

1428 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/3550454

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Copyright © 2022 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

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Published: 30 November 2022

Published in TOG Volume 41, Issue 6

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Lăzăroiu GGedeon TSzpilko DHalicka K(2024)Digital Twin-Based Alternate Ego Modeling and Simulation: Eva Herzigová as a 3D MetaHuman AvatarEngineering Management in Production and Services10.2478/emj-2024-002016:3(1-14)Online publication date: 1-Oct-2024
https://doi.org/10.2478/emj-2024-0020
Jiang YShen ZHong YGuo CWu YZhang YYu JXu L(2024)Robust Dual Gaussian Splatting for Immersive Human-centric Volumetric VideosACM Transactions on Graphics10.1145/368792643:6(1-15)Online publication date: 19-Dec-2024
https://dl.acm.org/doi/10.1145/3687926
Lin SLi ZSu ZZheng ZZhang HLiu Y(2024)LayGA: Layered Gaussian Avatars for Animatable Clothing TransferACM SIGGRAPH 2024 Conference Papers10.1145/3641519.3657501(1-11)Online publication date: 13-Jul-2024
https://dl.acm.org/doi/10.1145/3641519.3657501
Bashirov RLarionov AUstinova ESidorenko MSvitov DZakharkin ILempitsky V(2024)MoRF: Mobile Realistic Fullbody Avatars from a Monocular Video2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV57701.2024.00351(3533-3543)Online publication date: 3-Jan-2024
https://doi.org/10.1109/WACV57701.2024.00351
Jiang YShen ZWang PSu ZHong YZhang YYu JXu L(2024)HiFi4G: High-Fidelity Human Performance Rendering via Compact Gaussian Splatting2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.01866(19734-19745)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.01866
Li ZZheng ZWang LLiu Y(2024)Animatable Gaussians: Learning Pose-Dependent Gaussian Maps for High-Fidelity Human Avatar Modeling2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.01864(19711-19722)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.01864
Moon GWeipeng XJoshi RChenglei WShiratori T(2024)Authentic Hand Avatar from a Phone Scan via Universal Hand Model2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.00198(2029-2038)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.00198
Shetty AHabermann MSun GLuvizon DGolyanik VTheobalt C(2024)Holoported Characters: Real-Time Free-Viewpoint Rendering of Humans from Sparse RGB Cameras2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.00121(1206-1215)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.00121
Pang HZhu HKortylewski ATheobalt CHabermann M(2024)ASH: Animatable Gaussian Splats for Efficient and Photoreal Human Rendering2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.00117(1165-1175)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.00117
Guo JPrada FXiang DRomero JWu CPark HShiratori TSaito S(2024)Diffusion Shape Prior for Wrinkle-Accurate Cloth Registration2024 International Conference on 3D Vision (3DV)10.1109/3DV62453.2024.00042(790-799)Online publication date: 18-Mar-2024
https://doi.org/10.1109/3DV62453.2024.00042
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