DressUp!
DressUp!
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DOI：
10.1145/2366145.2366153
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发表时间：
2012-11
期刊：
影响因子：
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通讯作者：
L. Yu;Sai-Kit Yeung;Demetri Terzopoulos;T. Chan
中科院分区：
文献类型：
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作者：
L. Yu;Sai-Kit Yeung;Demetri Terzopoulos;T. Chan
提出了一种服装综合的自动优化方法。给定输入身体的头发颜色、眼睛颜色和肤色，再加上服装项目的衣柜，我们的服装合成系统建议一套符合特定着装要求的服装。我们介绍了一个用于建模和应用着装规范的概率框架，该框架利用贝叶斯网络对现实世界中服装的示例图像进行训练。然后，通过优化成本函数来获得合适的服装，该成本函数指导服装项目的选择，以最大化颜色兼容性和着装规范的适宜性。我们展示了我们在四个最常见的着装规范上的方法：休闲、运动装、商务-休闲和商务。在多个结果组合上验证的感知研究证明了我们框架的有效性。
We present an automatic optimization approach to outfit synthesis. Given the hair color, eye color, and skin color of the input body, plus a wardrobe of clothing items, our outfit synthesis system suggests a set of outfits subject to a particular dress code. We introduce a probabilistic framework for modeling and applying dress codes that exploits a Bayesian network trained on example images of real-world outfits. Suitable outfits are then obtained by optimizing a cost function that guides the selection of clothing items to maximize the color compatibility and dress code suitability. We demonstrate our approach on the four most common dress codes: Casual, Sportswear, Business-Casual, and Business. A perceptual study validated on multiple resultant outfits demonstrates the efficacy of our framework.