Probabilistic Model for Virtual Garment Modeling
Designing 3D garments is difficult, especially when the user lacks professional knowledge of garment design. Inspired by the assemble modeling, we facilitate 3D garment modeling by combining parts extracted from a database containing a large collection of garment component. A key challenge in assembly-based garment modeling is the identifying the relevant components that needs to be presented to the user. In this paper, we propose a virtual garment modeling method based on probabilistic model. We learn a probabilistic graphic model that encodes the semantic relationship among garment components from garment images. During the garment design process, the Bayesian graphic model is used to demonstrate the garment components that are semantically compatible with the existing model. And we also propose a new part stitching method for garment components. Our experiments indicates that the learned Bayesian graphic model increase the relevance of presented components and the part stitching method generates good results.
Garment modeling Bayesian Probabilistic Graphic Model and Part Stitching
Shah Zeng Fan Zhou Ruomei Wang Xiaonan Luo
National Engineering Research Center of Digital Life, State-Province Joint Laboratory of Digital Home Interactive Applications, School of Information Science Technology,Sun Yat-sen University, Guangzhou 510006, China
国际会议
9th Conference on Image and Graphics Technologies and Applications(IGTA2014)(第九届图像图形技术与应用学术会议)
北京
英文
212-221
2014-06-01（万方平台首次上网日期，不代表论文的发表时间）