TY - GEN
T1 - Virtual Garment Fitting Through Parsing and Context-Aware Generative Adversarial Networks with Discriminator Group
AU - Su, Wei Hong
AU - Chen, Sze Ann
AU - Chin, Chen I.
AU - Hsiao, Hsu Feng
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Owing to the rapid growth of the e-commerce industry, image-based virtual try-on has emerged as a popular research topic in recent years. Despite the introduction of multiple approaches to achieve this concept, there remains ample scope for research and improvement. In this regard, Generative Adversarial Networks (GANs) demonstrate a framework possessing immense potential for subsequent development. Nonetheless, the generated images reported in the literature often manifest blurred edges between semantic regions, thereby diminishing the credibility of results. Furthermore, the generation of try-on images may retain the original shape of the upper body clothing on the model mistakenly, such as the length and tightness of the torso, rather than adapting to the shape of the target clothing. In this paper, we propose a more comprehensive architecture to overcome the limitations of GAN-based approaches, which includes the following contributions. First, we introduce a new parsing and context generator that takes into account the warped binary mask of the geometric matching image of the target clothing. The outputs of this generator incorporate the generation of human parsing images that correspond to the generated try-on images. Moreover, we have designed a novel discriminator group that is specifically focused on judging whether the generated image is a reasonable representation of the specific clothing being worn. According to the experimental results, our method effectively exhibits better synthesis quality and remedies the common challenges encountered while using GANs for virtual try-on.
AB - Owing to the rapid growth of the e-commerce industry, image-based virtual try-on has emerged as a popular research topic in recent years. Despite the introduction of multiple approaches to achieve this concept, there remains ample scope for research and improvement. In this regard, Generative Adversarial Networks (GANs) demonstrate a framework possessing immense potential for subsequent development. Nonetheless, the generated images reported in the literature often manifest blurred edges between semantic regions, thereby diminishing the credibility of results. Furthermore, the generation of try-on images may retain the original shape of the upper body clothing on the model mistakenly, such as the length and tightness of the torso, rather than adapting to the shape of the target clothing. In this paper, we propose a more comprehensive architecture to overcome the limitations of GAN-based approaches, which includes the following contributions. First, we introduce a new parsing and context generator that takes into account the warped binary mask of the geometric matching image of the target clothing. The outputs of this generator incorporate the generation of human parsing images that correspond to the generated try-on images. Moreover, we have designed a novel discriminator group that is specifically focused on judging whether the generated image is a reasonable representation of the specific clothing being worn. According to the experimental results, our method effectively exhibits better synthesis quality and remedies the common challenges encountered while using GANs for virtual try-on.
UR - http://www.scopus.com/inward/record.url?scp=85180007855&partnerID=8YFLogxK
U2 - 10.1109/APSIPAASC58517.2023.10317305
DO - 10.1109/APSIPAASC58517.2023.10317305
M3 - Conference contribution
AN - SCOPUS:85180007855
T3 - 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023
SP - 1732
EP - 1738
BT - 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023
Y2 - 31 October 2023 through 3 November 2023
ER -