  Image-based virtual try-on aims to fit an in-shop garment onto a clothed
person image. Garment warping, which aligns the target garment with the
corresponding body parts in the person image, is a crucial step in achieving
this goal. Existing methods often use multi-stage frameworks to handle clothes
warping, person body synthesis and tryon generation separately or rely on noisy
intermediate parser-based labels. We propose a novel single-stage framework
that implicitly learns the same without explicit multi-stage learning. Our
approach utilizes a novel semantic-contextual fusion attention module for
garment-person feature fusion, enabling efficient and realistic cloth warping
and body synthesis from target pose keypoints. By introducing a lightweight
linear attention framework that attends to garment regions and fuses multiple
sampled flow fields, we also address misalignment and artifacts present in
previous methods. To achieve simultaneous learning of warped garment and try-on
results, we introduce a Warped Cloth Learning Module. Our proposed approach
significantly improves the quality and efficiency of virtual try-on methods,
providing users with a more reliable and realistic virtual try-on experience.
