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2022 ◽  
Vol 48 ◽  
pp. 104022
Jingguo Xue ◽  
Xueliang Hou ◽  
Jianli Zhou ◽  
Xiaobing Liu ◽  
Yu Guo

2022 ◽  
Vol 41 (1) ◽  
pp. 1-17
Xin Chen ◽  
Anqi Pang ◽  
Wei Yang ◽  
Peihao Wang ◽  
Lan Xu ◽  

In this article, we present TightCap, a data-driven scheme to capture both the human shape and dressed garments accurately with only a single three-dimensional (3D) human scan, which enables numerous applications such as virtual try-on, biometrics, and body evaluation. To break the severe variations of the human poses and garments, we propose to model the clothing tightness field—the displacements from the garments to the human shape implicitly in the global UV texturing domain. To this end, we utilize an enhanced statistical human template and an effective multi-stage alignment scheme to map the 3D scan into a hybrid 2D geometry image. Based on this 2D representation, we propose a novel framework to predict clothing tightness field via a novel tightness formulation, as well as an effective optimization scheme to further reconstruct multi-layer human shape and garments under various clothing categories and human postures. We further propose a new clothing tightness dataset of human scans with a large variety of clothing styles, poses, and corresponding ground-truth human shapes to stimulate further research. Extensive experiments demonstrate the effectiveness of our TightCap to achieve the high-quality human shape and dressed garments reconstruction, as well as the further applications for clothing segmentation, retargeting, and animation.

2022 ◽  
Vol 49 ◽  
pp. 101702
Aditi Wagle ◽  
Michael J. Angove ◽  
Asmita Mahara ◽  
Amrita Wagle ◽  
Bandita Mainali ◽  

2022 ◽  
Vol 155 ◽  
pp. 111696
Attila Szolnoki ◽  
Xiaojie Chen

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