CSSNet: image-based clothing style switch

Shao-Pin Huang; Der-Lor Way; Zen-Chung Shih

doi:10.1117/12.2566971

1 June 2020 CSSNet: image-based clothing style switch

Shao-Pin Huang, Der-Lor Way, Zen-Chung Shih

Proceedings Volume 11515, International Workshop on Advanced Imaging Technology (IWAIT) 2020; 1151520 (2020) https://doi.org/10.1117/12.2566971
Event: International Workshop on Advanced Imaging Technologies 2020 (IWAIT 2020), 2020, Yogyakarta, Indonesia

Abstract

We propose a framework, the CSSNet to exchange the upper clothes across people with different pose, body shape and clothing. We present an approach consists of three stages. (1) Disentangling the features, such as cloth, body pose and semantic segmentation from source and target person. (2) Synthesizing realistic and high resolution target dressing style images. (3) Transfer the complex logo from source clothing to target wearing. Our proposed end-to-end neural network architecture which can generate the specific person to wear the target clothing. In addition, we also propose a post process method to recover the complex logos on network outputs which are missing or blurring. Our results display more realistic and higher quality than previous methods. Our method can also preserve cloth shape and texture simultaneously.

Citation Download Citation

Shao-Pin Huang, Der-Lor Way, and Zen-Chung Shih "CSSNet: image-based clothing style switch", Proc. SPIE 11515, International Workshop on Advanced Imaging Technology (IWAIT) 2020, 1151520 (1 June 2020); https://doi.org/10.1117/12.2566971

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available