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A data-driven editing framework for automatic 3D garment modeling

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Abstract

Exploring shape variations on virtual garments is significant but challenging to the aspect of 3D garment modeling. In this paper, we propose a data-driven editing framework for automatic 3D garment modeling, which includes semantic garment segmentation, probabilistic reasoning for component suggestion, and garment component merging. The key idea in this work is to develop a simple but effective garment synthesis that utilizes a continuous style description, which can be characterized by the ratio of area and boundary length on garment components. First, a semi-supervised learning algorithm is proposed to simultaneously segment and label the components in 3D garments. Second, a set of matchable probability measurement is applied to recommend components that can be regarded as a new 3D garment. Third, a variation synthesis is developed to satisfy the garment style criteria while ensuring the realistic-looking plausibility of the results. As demonstrated by the experiments, our method is able to generate various reasonable garments with material effects to enrich existing 3D garments.

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Acknowledgments

This research is supported by the National Natural Science Foundation of China (61462051, 61462056, 61272192, 61502541, 81560296), the Applied Fundamental Research Project of Yunnan Province (2014FB133), the Applied Fundamental Research Key Project of Yunnan Province(2014FA028), the Science Research Funded Project of Kunming University of Science and Technology (KKSY201403119).

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Authors and Affiliations

  1. Computer Technology Application Key Laboratory of Yunnan Province, Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, China

    Li Liu, Xiaodong Fu & Lijun Liu

  2. School of Data and Computer Science, National Engineering Research Center of Digital Life, Sun Yat-sen University, Guangzhou, 510006, China

    Zhuo Su & Ruomei Wang

  3. National Engineering Research Center of Digital Life, School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, 510006, China

    Xiaonan Luo

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  1. Li Liu
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Correspondence to Li Liu.

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Liu, L., Su, Z., Fu, X. et al. A data-driven editing framework for automatic 3D garment modeling. Multimed Tools Appl 76, 12597–12626 (2017). https://doi.org/10.1007/s11042-016-3688-4

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