PFDM: Parser-Free Virtual Try-on via Diffusion Model
Yunfang Niu Dong Yi Lingxiang Wu Zhiwei Liu Pengxiang Cai Jinqiao Wang
Virtual try-on can significantly improve the garment shopping experiences in
both online and in-store scenarios, attracting broad interest in computer
vision. However, to achieve high-fidelity try-on performance, most
state-of-the-art methods still rely on accurate segmentation masks, which are
often produced by near-perfect parsers or manual labeling. To overcome the
bottleneck, we propose a parser-free virtual try-on method based on the
diffusion model (PFDM). Given two images, PFDM can "wear" garments on the
target person seamlessly by implicitly warping without any other information.
To learn the model effectively, we synthesize many pseudo-images and construct
sample pairs by wearing various garments on persons. Supervised by the
large-scale expanded dataset, we fuse the person and garment features using a
proposed Garment Fusion Attention (GFA) mechanism. Experiments demonstrate that
our proposed PFDM can successfully handle complex cases, synthesize
high-fidelity images, and outperform both state-of-the-art parser-free and
parser-based models.