이미지를 사용한 가상의상착용 알고리즘들의 성능 분석

VTON (Virtual try-on) is a key technology that can activate the online commerce of fashion items. However, the early 3D graphics-based methods require the 3D information of the clothing or the human body, which is difficult to secure realistically. In order to overcome this problem, Image-based deep-learning algorithms such as VITON (Virtual image try-on) and CP-VTON (Characteristic preserving-virtual try-on) has been published, but only a sampled results on performance is presented. In order to examine the strength and weakness for their commercialization, the performance analysis is needed according to the complexity of the clothes, the object posture and body shape, and the degree of occlusion of the clothes. In this paper, IoU and SSIM were evaluated for the performance of transformation and synthesis stages, together with non-DL SCM based method. As a result, CP-VTON shows the best performance, but its performance varies significantly according to posture and complexity of clothes. The reasons for this were attributed to the limitations of secondary geometric deformation and the limitations of the synthesis technology through GAN.