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A Semantic Parametric Model for 3D Human Body Reshaping.

Song, D., Jin, Y., Wang, T., Li, C., Tong, R. and Chang, J., 2019. A Semantic Parametric Model for 3D Human Body Reshaping. In: Edutainment 2018: International Conference on E-Learning and Games., 28-30 June, Xi'an, China, 169 - 176.

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A Semantic Parametric Model.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial.


DOI: 10.1007/978-3-030-23712-7_24


Semantic human body reshaping builds a 3D body according to several anthropometric measurements, playing important roles in virtual fitting and human body design. We propose a novel part-based semantic body model for 3D body reshaping. We adopt 20 types of measurements in regard of length and girth information of body shape. Our approach takes any number (1–20) of measurements as input, and generates a 3D human body. Firstly, all missing measurements are estimated from known measurements using a correlation-based method. Then, based on our proposed semantic model, we learn corresponding semantic body parameters which determine a 3D body from measurements. Our model is trained using a database of 4000 registered body meshes which are fitted with scans of real human bodies. Through experiments, we compare our approach with previous methods and show the advantages of our model.

Item Type:Conference or Workshop Item (Paper)
Group:Faculty of Media & Communication
ID Code:33006
Deposited By: Symplectic RT2
Deposited On:11 Nov 2019 11:50
Last Modified:14 Mar 2022 14:18


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