Structure-Aware Flow Generation for Human Body Reshaping

Ren, Jianqiang; Yao, Yuan; Lei, Biwen; Cui, Miaomiao; Xie, Xuansong

doi:10.1109/cvpr52688.2022.00760

Cited by 3 publications

(1 citation statement)

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“…Similar to our work, [23] uses deformation fields to change a person's appearance. Their motivation is for photo retouching, whereas our method focuses on changing garment sizing.…”

Section: Deformable Transformsmentioning

confidence: 98%

SizeGAN: Improving Size Representation in Clothing Catalogs

Lewis¹,

Guttag²

2022

Preprint

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Online clothing catalogs lack diversity in body shape and garment size. Brands commonly display their garments on models of one or two sizes, rarely including plus-size models. In this work, we propose a new method, SizeGAN, for generating images of garments on different-sized models. To change the garment and model size while maintaining a photorealistic image, we incorporate image alignment ideas from the medical imaging literature into the StyleGAN2-ADA architecture. Our method learns deformation fields at multiple resolutions and uses a spatial transformer to modify the garment and model size. We evaluate our approach along three dimensions: realism, garment faithfulness, and size. To our knowledge, SizeGAN is the first method to focus on this size under-representation problem for modeling clothing. We provide an analysis comparing SizeGAN to other plausible approaches and additionally provide the first clothing dataset with size labels. In a user study comparing SizeGAN and two recent virtual tryon methods, we show that our method ranks first in each dimension, and was vastly preferred for realism and garment faithfulness. In comparison to most previous work, which has focused on generating photorealistic images of garments, our work shows that it is possible to generate images that are both photorealistic and cover diverse garment sizes.

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“…Similar to our work, [23] uses deformation fields to change a person's appearance. Their motivation is for photo retouching, whereas our method focuses on changing garment sizing.…”

Section: Deformable Transformsmentioning

confidence: 98%