A deep learning based interactive sketching system for fashion images design

Yao Li, Xianggang Yu, Xiaoguang Han, Nianjuan Jiang, K. Jia, Jiangbo Lu
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引用次数: 4

Abstract

In this work, we propose an interactive system to design diverse high-quality garment images from fashion sketches and the texture information. The major challenge behind this system is to generate high-quality and detailed texture according to the user-provided texture information. Prior works mainly use the texture patch representation and try to map a small texture patch to a whole garment image, hence unable to generate high-quality details. In contrast, inspired by intrinsic image decomposition, we decompose this task into texture synthesis and shading enhancement. In particular, we propose a novel bi-colored edge texture representation to synthesize textured garment images and a shading enhancer to render shading based on the grayscale edges. The bi-colored edge representation provides simple but effective texture cues and color constraints, so that the details can be better reconstructed. Moreover, with the rendered shading, the synthesized garment image becomes more vivid.
基于深度学习的时尚图像设计交互素描系统
在这项工作中,我们提出了一个交互系统,从时装草图和纹理信息中设计出各种高质量的服装图像。该系统面临的主要挑战是根据用户提供的纹理信息生成高质量和详细的纹理。以往的作品主要使用纹理补丁表示,试图将一个小的纹理补丁映射到整幅服装图像,因此无法生成高质量的细节。相反,受图像固有分解的启发,我们将该任务分解为纹理合成和阴影增强。特别地,我们提出了一种新的双色边缘纹理表示来合成纹理服装图像,并提出了一种基于灰度边缘的阴影增强器来渲染阴影。双色边缘表示提供了简单而有效的纹理线索和颜色约束,从而可以更好地重建细节。此外,通过渲染阴影,合成的服装图像更加逼真。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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