In this paper, we first investigate a visual quality degradation problem
observed in recent high-resolution virtual try-on approach. The tendency is
empirically found that the textures of clothes are squeezed at the sleeve, as
visualized in the upper row of Fig.1(a). A main reason for the issue arises
from a gradient conflict between two popular losses, the Total Variation (TV)
and adversarial losses. Specifically, the TV loss aims to disconnect boundaries
between the sleeve and torso in a warped clothing mask, whereas the adversarial
loss aims to combine between them. Such contrary objectives feedback the
misaligned gradients to a cascaded appearance flow estimation, resulting in
undesirable squeezing artifacts. To reduce this, we propose a Sequential
Deformation (SD-VITON) that disentangles the appearance flow prediction layers
into TV objective-dominant (TVOB) layers and a task-coexistence (TACO) layer.
Specifically, we coarsely fit the clothes onto a human body via the TVOB
layers, and then keep on refining via the TACO layer. In addition, the bottom
row of Fig.1(a) shows a different type of squeezing artifacts around the waist.
To address it, we further propose that we first warp the clothes into a
tucked-out shirts style, and then partially erase the texture from the warped
clothes without hurting the smoothness of the appearance flows. Experimental
results show that our SD-VITON successfully resolves both types of artifacts
and outperforms the baseline methods. Source code will be available at
this https URL

研究表明最近的高分辨率虚拟试穿方法存在视觉质量下降问题，主要原因是总变差和对抗损失之间的梯度冲突，为了解决这个问题，提出了一种序列变形方法（SD-VITON），成功解决了衣袖和腰部的挤压伪影问题，并优于基线方法。