Garment Avatars: Realistic Cloth Driving using Pattern Registration

• URL: http://arxiv.org/abs/2206.03373v1
• Date: Tue, 7 Jun 2022 15:06:55 GMT
• Title: Garment Avatars: Realistic Cloth Driving using Pattern Registration
• Authors: Oshri Halimi, Fabian Prada, Tuur Stuyck, Donglai Xiang, Timur Bagautdinov, He Wen, Ron Kimmel, Takaaki Shiratori, Chenglei Wu, Yaser Sheikh
• Abstract summary: We propose an end-to-end pipeline for building drivable representations for clothing. A Garment Avatar is an expressive and fully-drivable geometry model for a piece of clothing. We demonstrate the efficacy of our pipeline on a realistic virtual telepresence application.
• Score: 39.936812232884954
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Virtual telepresence is the future of online communication. Clothing is an essential part of a person's identity and self-expression. Yet, ground truth data of registered clothes is currently unavailable in the required resolution and accuracy for training telepresence models for realistic cloth animation. Here, we propose an end-to-end pipeline for building drivable representations for clothing. The core of our approach is a multi-view patterned cloth tracking algorithm capable of capturing deformations with high accuracy. We further rely on the high-quality data produced by our tracking method to build a Garment Avatar: an expressive and fully-drivable geometry model for a piece of clothing. The resulting model can be animated using a sparse set of views and produces highly realistic reconstructions which are faithful to the driving signals. We demonstrate the efficacy of our pipeline on a realistic virtual telepresence application, where a garment is being reconstructed from two views, and a user can pick and swap garment design as they wish. In addition, we show a challenging scenario when driven exclusively with body pose, our drivable garment avatar is capable of producing realistic cloth geometry of significantly higher quality than the state-of-the-art.

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