  The goal of image-based virtual try-on is to generate an image of the target
person naturally wearing the given clothing. However, most existing methods
solely focus on the frontal try-on using the frontal clothing. When the views
of the clothing and person are significantly inconsistent, particularly when
the person's view is non-frontal, the results are unsatisfactory. To address
this challenge, we introduce Multi-View Virtual Try-ON (MV-VTON), which aims to
reconstruct the dressing results of a person from multiple views using the
given clothes. On the one hand, given that single-view clothes provide
insufficient information for MV-VTON, we instead employ two images, i.e., the
frontal and back views of the clothing, to encompass the complete view as much
as possible. On the other hand, the diffusion models that have demonstrated
superior abilities are adopted to perform our MV-VTON. In particular, we
propose a view-adaptive selection method where hard-selection and
soft-selection are applied to the global and local clothing feature extraction,
respectively. This ensures that the clothing features are roughly fit to the
person's view. Subsequently, we suggest a joint attention block to align and
fuse clothing features with person features. Additionally, we collect a MV-VTON
dataset, i.e., Multi-View Garment (MVG), in which each person has multiple
photos with diverse views and poses. Experiments show that the proposed method
not only achieves state-of-the-art results on MV-VTON task using our MVG
dataset, but also has superiority on frontal-view virtual try-on task using
VITON-HD and DressCode datasets. Codes and datasets will be publicly released
at https://github.com/hywang2002/MV-VTON .
