The idea of \textit{Virtual Try-ON} (VTON) benefits e-retailing by giving an user the convenience of trying a clothing at the comfort of their home. In general, most of the existing VTON methods produce inconsistent results when a person posing with his arms folded i.e., bent or crossed, wants to try an outfit. The problem becomes severe in the case of long-sleeved outfits. As then, for crossed arm postures, overlap among different clothing parts might happen. The existing approaches, especially the warping-based methods employing \textit{Thin Plate Spline (TPS)} transform can not tackle such cases. To this end, we attempt a solution approach where the clothing from the source person is segmented into semantically meaningful parts and each part is warped independently to the shape of the person. To address the bending issue, we employ hand-crafted geometric features consistent with human body geometry for warping the source outfit. In addition, we propose two learning-based modules: a synthesizer network and a mask prediction network. All these together attempt to produce a photo-realistic, pose-robust VTON solution without requiring any paired training data. Comparison with some of the benchmark methods clearly establishes the effectiveness of the approach.
翻译:\ textit{ 虚拟尝试- ON} (Vtonton) 的理念有利于电子零售, 使用户能够方便地在家里安心地尝试衣物。 一般来说, 现有的Vton 方法大多产生不一致的结果, 当一个人用手臂折叠起来, 即弯曲或交叉折叠, 想要尝试一件衣服。 问题在长袖装扮的情况下变得很严重。 和那时一样, 对于跨臂姿势, 不同服装部分之间可能会发生重叠。 现有的方法, 特别是使用\ textit{ Thin Plate Spline( TPS) 转换的扭曲方法, 无法解决这类案例。 为此, 我们尝试一种解决方案, 将来源人的衣物分割成具有语义意义的部分, 并且每个部分都独立地与个人形状发生冲突。 为了解决弯曲问题, 我们使用手工制作的地理测量特征与人体形状的测量方法来扭曲源体结构。 此外, 我们提出两个基于学习的模块: 合成器网络和面具预测网络。 所有这些方法都试图用光基化的方法 来建立光- 。