Improving Diffusion Models for Virtual Try-on

1 code implementation • 8 Mar 2024 • Yisol Choi, Sangkyung Kwak, Kyungmin Lee, Hyungwon Choi, Jinwoo Shin

Finally, we present a customization method using a pair of person-garment images, which significantly improves fidelity and authenticity.

Virtual Try-on

Adiabatic Persistent Contrastive Divergence Learning

no code implementations • 26 May 2016 • Hyeryung Jang, Hyungwon Choi, Yung Yi, Jinwoo Shin

This paper studies the problem of parameter learning in probabilistic graphical models having latent variables, where the standard approach is the expectation maximization algorithm alternating expectation (E) and maximization (M) steps.