Street TryOn: Learning In-the-Wild Virtual Try-On from Unpaired Person Images

Aiyu Cui, Jay Mahajan, Viraj Shah, Preeti Gomathinayagam, Chang Liu, Svetlana Lazebnik; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 1414-1423

Most virtual try-on research is motivated to serve the fashion business by generating images to demonstrate garments on studio models at a lower cost. However virtual try-on should be a broader application that also allows customers to visualize garments on themselves using their own casual photos known as in-the-wild try-on. Unfortunately the existing methods which achieve plausible results for studio try-on settings perform poorly in the in-the-wild context. This is because these methods often require paired images (garment images paired with images of people wearing the same garment) for training. While such paired data is easy to collect from shopping websites for studio settings it is difficult to obtain for in-the-wild scenes. In this work we fill the gap by (1) introducing a StreetTryOn benchmark to support in-the-wild virtual try-on applications and (2) proposing a novel method to learn virtual try-on from a set of in-the-wild person images directly without requiring paired data. We tackle the unique challenges including warping garments to more diverse human poses and rendering more complex backgrounds faithfully by a novel DensePose warping correction method combined with diffusion-based conditional inpainting. Our experiments show competitive performance for standard studio try-on tasks and SOTA performance for street try-on and cross-domain try-on tasks.