Style-Based Global Appearance Flow for Virtual Try-On

• URL: http://arxiv.org/abs/2204.01046v1
• Date: Sun, 3 Apr 2022 10:58:04 GMT
• Title: Style-Based Global Appearance Flow for Virtual Try-On
• Authors: Sen He, Yi-Zhe Song, Tao Xiang
• Abstract summary: A novel global appearance flow estimation model is proposed in this work. Experiment results on a popular virtual try-on benchmark show that our method achieves new state-of-the-art performance.
• Score: 119.95115739956661
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Image-based virtual try-on aims to fit an in-shop garment into a clothed person image. To achieve this, a key step is garment warping which spatially aligns the target garment with the corresponding body parts in the person image. Prior methods typically adopt a local appearance flow estimation model. They are thus intrinsically susceptible to difficult body poses/occlusions and large mis-alignments between person and garment images (see Fig.~\ref{fig:fig1}). To overcome this limitation, a novel global appearance flow estimation model is proposed in this work. For the first time, a StyleGAN based architecture is adopted for appearance flow estimation. This enables us to take advantage of a global style vector to encode a whole-image context to cope with the aforementioned challenges. To guide the StyleGAN flow generator to pay more attention to local garment deformation, a flow refinement module is introduced to add local context. Experiment results on a popular virtual try-on benchmark show that our method achieves new state-of-the-art performance. It is particularly effective in a `in-the-wild' application scenario where the reference image is full-body resulting in a large mis-alignment with the garment image (Fig.~\ref{fig:fig1} Top). Code is available at: \url{https://github.com/SenHe/Flow-Style-VTON}.

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