Size Does Matter: Size-aware Virtual Try-on via Clothing-oriented Transformation Try-on Network

Chieh-Yun Chen, Yi-Chung Chen, Hong-Han Shuai, Wen-Huang Cheng; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023, pp. 7513-7522

Virtual try-on tasks aim at synthesizing realistic try-on results by trying target clothes on humans. Most previous works relied on the Thin Plate Spline or appearance flows to warp clothes to fit human body shapes. However, both approaches cannot handle complex warping, leading to over distortion or misalignment. Furthermore, there is a critical unaddressed challenge of adjusting clothing sizes for try-on. To tackle these issues, we propose a Clothing-Oriented Transformation Try-On Network (COTTON). COTTON leverages clothing structure with landmarks and segmentation to design a novel landmark-guided transformation for precisely deforming clothes, allowing for size adjustment during try-on. Additionally, to properly remove the clothing region from the human image without losing significant human characteristics, we propose a clothing elimination policy based on both transformed clothes and human segmentation. This method enables users to try on clothes tucked-in or untucked while retaining more human characteristics. Both qualitative and quantitative results show that COTTON outperforms the state-of-the-art high-resolution virtual try-on approaches. All the code is available at https://github.com/cotton6/COTTON-size-does-matter.