ACDG-VTON: Accurate and Contained Diffusion Generation for Virtual
  Try-On
Jeffrey Zhang Kedan Li Shao-Yu Chang David Forsyth
Virtual Try-on (VTON) involves generating images of a person wearing selected
garments. Diffusion-based methods, in particular, can create high-quality
images, but they struggle to maintain the identities of the input garments. We
identified this problem stems from the specifics in the training formulation
for diffusion. To address this, we propose a unique training scheme that limits
the scope in which diffusion is trained. We use a control image that perfectly
aligns with the target image during training. In turn, this accurately
preserves garment details during inference. We demonstrate our method not only
effectively conserves garment details but also allows for layering, styling,
and shoe try-on. Our method runs multi-garment try-on in a single inference
cycle and can support high-quality zoomed-in generations without training in
higher resolutions. Finally, we show our method surpasses prior methods in
accuracy and quality.