Abstract
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.
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Zeng, S., Zhou, F., Wang, R., Luo, X. (2014). Probabilistic Model for Virtual Garment Modeling. In: Tan, T., Ruan, Q., Wang, S., Ma, H., Huang, K. (eds) Advances in Image and Graphics Technologies. IGTA 2014. Communications in Computer and Information Science, vol 437. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45498-5_23
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DOI: https://doi.org/10.1007/978-3-662-45498-5_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45497-8
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