OOTDiffusion: Outfitting Fusion based Latent Diffusion for Controllable
  Virtual Try-on
Authors: name: Yuhao Xu, name: Tao Gu, name: Weifeng Chen, name: Chengcai Chen

Image-based virtual try-on (VTON), which aims to generate an outfitted image
of a target human wearing an in-shop garment, is a challenging image-synthesis
task calling for not only high fidelity of the outfitted human but also full
preservation of garment details. To tackle this issue, we propose Outfitting
over Try-on Diffusion (OOTDiffusion), leveraging the power of pretrained latent
diffusion models and designing a novel network architecture for realistic and
controllable virtual try-on. Without an explicit warping process, we propose an
outfitting UNet to learn the garment detail features, and merge them with the
target human body via our proposed outfitting fusion in the denoising process
of diffusion models. In order to further enhance the controllability of our
outfitting UNet, we introduce outfitting dropout to the training process, which
enables us to adjust the strength of garment features through classifier-free
guidance. Our comprehensive experiments on the VITON-HD and Dress Code datasets
demonstrate that OOTDiffusion efficiently generates high-quality outfitted
images for arbitrary human and garment images, which outperforms other VTON
methods in both fidelity and controllability, indicating an impressive
breakthrough in virtual try-on. Our source code is available at
https://github.com/levihsu/OOTDiffusion.