Self-Adaptive Clothing Mapping Based Virtual Try-on

被引:0
作者
Shen, Chengji [1 ]
Liu, Zhenjiang [2 ]
Gao, Xin [2 ]
Feng, Zunlei [1 ,3 ]
Song, Mingli [1 ,3 ]
机构
[1] Zhejiang Univ, Hangzhou, Zhejiang, Peoples R China
[2] Alibaba Grp, Hangzhou, Zhejiang, Peoples R China
[3] ZJU Bangsun Joint Res Ctr, Hangzhou, Zhejiang, Peoples R China
基金
中国国家自然科学基金;
关键词
Virtual try-on; self-adaptive; clothing mapping; color difference;
D O I
10.1145/3613453
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
VTON (Virtual Try-ON), as an innovative visual application in e-commerce scenarios with great commercial value, has been widely studied in recent years. Due to its better robustness and realistic effect, deformation synthesize-based VTON has become the dominant approach in this field. Existing clothing deformation techniques optimize the mapping relations between the original clothing image and the ground truth (GT) image of the worn clothing. However, there are color differences between the original and GT clothing images caused by lighting, warping, and occlusion. The color differences may lead to misaligned clothing mapping by only minimizing the cost of pixel value difference. Another drawback is that taking the parsing prediction as GT will bring alignment remnant, rooting in the processing order of parsing and deformation. Aiming above two drawbacks, we put forward SAME-VTON (Self-Adaptive clothing Mapping basEd Virtual Try-ON) for achieving realistic virtual try-on results. The core of SAME-VTON is the self-adaptive clothing mapping technique, composed of two parts: a color-adaptive clothing mapping module and a parsing-adaptive prediction process. In the color-adaptive clothing mapping module, we map each pixel of the target clothing with a combination of multiple pixel values from the original clothing image, which considers both the position and color changes. Furthermore, different combination weights are learned to increase the diversity of color mapping. In the parsing-adaptive prediction process, the color-adaptive clothing mapping module is adopted to deform clothing first, then the human parsing result is predicted under the reference of the deformed clothing, which can avoid alignment remnant. Extensive experiments demonstrate that the proposed SAME-VTON with the self-adaptive clothing mapping technique can achieve optimal mapping in the case of large color differences and obtain superior results compared with existing deformation-synthesize-based VTON.
引用
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页数:26
相关论文
共 68 条
[11]   VITON-GT: An Image-based Virtual Try-On Model with Geometric Transformations [J].
Fincato, Matteo ;
Landi, Federico ;
Cornia, Marcella ;
Cesari, Fabio ;
Cucchiara, Rita .
2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR), 2021, :7669-7676
[12]   Shape Controllable Virtual Try-on for Underwear Models [J].
Gao, Xin ;
Liu, Zhenjiang ;
Feng, Zunlei ;
Shen, Chengji ;
Ou, Kairi ;
Tang, Haihong ;
Song, Mingli .
PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA, MM 2021, 2021, :563-572
[13]   Disentangled Cycle Consistency for Highly-realistic Virtual Try-On [J].
Ge, Chongjian ;
Song, Yibing ;
Ge, Yuying ;
Yang, Han ;
Liu, Wei ;
Luo, Ping .
2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021, 2021, :16923-16932
[14]   Parser-Free Virtual Try-on via Distilling Appearance Flows [J].
Ge, Yuying ;
Song, Yibing ;
Zhang, Ruimao ;
Ge, Chongjian ;
Liu, Wei ;
Luo, Ping .
2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021, 2021, :8481-8489
[15]  
Goodfellow IJ, 2014, ADV NEUR IN, V27, P2672
[16]   PAINT: Photo-realistic Fashion Design Synthesis [J].
Gu, Xiaoling ;
Huang, Jie ;
Wong, Yongkang ;
Yu, Jun ;
Fan, Jianping ;
Peng, Pai ;
Kankanhalli, Mohan S. .
ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS, 2024, 20 (02)
[17]   DensePose: Dense Human Pose Estimation In The Wild [J].
Guler, Riza Alp ;
Neverova, Natalia ;
Kokkinos, Lasonas .
2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2018, :7297-7306
[18]  
Han XT, 2018, Arxiv, DOI arXiv:1711.08447
[19]   ClothFlow: A Flow-Based Model for Clothed Person Generation [J].
Han, Xintong ;
Hu, Xiaojun ;
Huang, Weilin ;
Scott, Matthew R. .
2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2019), 2019, :10470-10479
[20]   Deep Residual Learning for Image Recognition [J].
He, Kaiming ;
Zhang, Xiangyu ;
Ren, Shaoqing ;
Sun, Jian .
2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2016, :770-778