Virtual Try-On for Cultural Clothing: A Benchmarking Study
This work addresses the problem of limited cultural diversity in virtual try-on systems for users interested in non-Western clothing styles. It is an incremental step towards more inclusive try-on technology.
The authors introduce BD-VITON, a new virtual try-on dataset featuring Bangladeshi garments like saree and panjabi, addressing the lack of cultural diversity in existing benchmarks. They retrain and evaluate three try-on models (StableViton, HR-VITON, VITON-HD) on this dataset, demonstrating consistent improvements over zero-shot inference.
Although existing virtual try-on systems have made significant progress with the advent of diffusion models, the current benchmarks of these models are based on datasets that are dominant in western-style clothing and female models, limiting their ability to generalize culturally diverse clothing styles. In this work, we introduce BD-VITON, a virtual try-on dataset focused on Bangladeshi garments, including saree, panjabi and salwar kameez, covering both male and female categories as well. These garments present unique structural challenges such as complex draping, asymmetric layering, and high deformation complexities which are underrepresented in the original VITON dataset. To establish strong baselines, we retrain and evaluate try-on models, namely StableViton, HR-VITON, and VITON-HD on our dataset. Our experiments demonstrate consistent improvements in terms of both quantitative and qualitative analysis, compared to zero shot inference.