Style-Based Global Appearance Flow for Virtual Try-On
This addresses virtual try-on for e-commerce by improving garment alignment in challenging poses and occlusions, though it is incremental as it builds on prior flow estimation methods.
The paper tackles the problem of garment warping in virtual try-on by proposing a global appearance flow estimation model using a StyleGAN-based architecture, achieving state-of-the-art performance on a benchmark and showing effectiveness in 'in-the-wild' scenarios with large mis-alignments.
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}.