Outfit Anyone

Virtual try-on has become a transformative technology, empowering users to experiment with fashion without ever having to physically try on clothing. However, existing methods often struggle with generating high-fidelity and detail-consistent results. Diffusion models have demonstrated their ability to generate high-quality and photorealistic images, but when it comes to conditional generation scenarios like virtual try-ons, they still face challenges in achieving control and consistency.

Outfit Anyone addresses these limitations by leveraging a two-stream conditional diffusion model, enabling it to adeptly handle garment deformation for more lifelike results. It distinguishes itself with scalability—modulating factors such as pose and body shape—and broad applicability, extending from anime to in-the-wild images. Outfit Anyone's performance in diverse scenarios underscores its utility and readiness for real-world deployment.