WAS-VTON: Warping Architecture Search for Virtual Try-on Network

• URL: http://arxiv.org/abs/2108.00386v1
• Date: Sun, 1 Aug 2021 07:52:56 GMT
• Title: WAS-VTON: Warping Architecture Search for Virtual Try-on Network
• Authors: Zhenyu Xie, Xujie Zhang, Fuwei Zhao, Haoye Dong, Michael C. Kampffmeyer, Haonan Yan, Xiaodan Liang
• Abstract summary: We introduce a NAS-Warping Module and elaborately design a bilevel hierarchical search space. We learn a combination of repeatable warping cells and convolution operations specifically for the clothing-person alignment. A NAS-Fusion Module is proposed to synthesize more natural final try-on results.
• Score: 57.52118202523266
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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