  We present M&M VTO, a mix and match virtual try-on method that takes as input
multiple garment images, text description for garment layout and an image of a
person. An example input includes: an image of a shirt, an image of a pair of
pants, "rolled sleeves, shirt tucked in", and an image of a person. The output
is a visualization of how those garments (in the desired layout) would look
like on the given person. Key contributions of our method are: 1) a single
stage diffusion based model, with no super resolution cascading, that allows to
mix and match multiple garments at 1024x512 resolution preserving and warping
intricate garment details, 2) architecture design (VTO UNet Diffusion
Transformer) to disentangle denoising from person specific features, allowing
for a highly effective finetuning strategy for identity preservation (6MB model
per individual vs 4GB achieved with, e.g., dreambooth finetuning); solving a
common identity loss problem in current virtual try-on methods, 3) layout
control for multiple garments via text inputs specifically finetuned over
PaLI-3 for virtual try-on task. Experimental results indicate that M&M VTO
achieves state-of-the-art performance both qualitatively and quantitatively, as
well as opens up new opportunities for virtual try-on via language-guided and
multi-garment try-on.
