  Despite recent progress on image-based virtual try-on, current methods are
constraint by shared warping networks and thus fail to synthesize natural
try-on results when faced with clothing categories that require different
warping operations. In this paper, we address this problem by finding clothing
category-specific warping networks for the virtual try-on task via Neural
Architecture Search (NAS). We introduce a NAS-Warping Module and elaborately
design a bilevel hierarchical search space to identify the optimal
network-level and operation-level flow estimation architecture. Given the
network-level search space, containing different numbers of warping blocks, and
the operation-level search space with different convolution operations, we
jointly learn a combination of repeatable warping cells and convolution
operations specifically for the clothing-person alignment. Moreover, a
NAS-Fusion Module is proposed to synthesize more natural final try-on results,
which is realized by leveraging particular skip connections to produce
better-fused features that are required for seamlessly fusing the warped
clothing and the unchanged person part. We adopt an efficient and stable
one-shot searching strategy to search the above two modules. Extensive
experiments demonstrate that our WAS-VTON significantly outperforms the
previous fixed-architecture try-on methods with more natural warping results
and virtual try-on results.
