A Semantic Parametric Model for 3D Human Body Reshaping

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Authors: Song, D., Jin, Y., Wang, T., Li, C., Tong, R. and Chang, J.

http://eprints.bournemouth.ac.uk/33006/

Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume: 11462 LNCS

Pages: 169-176

eISSN: 1611-3349

ISBN: 9783030237110

ISSN: 0302-9743

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

© 2019, Springer Nature Switzerland AG. 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.

The data on this page was last updated at 05:19 on October 21, 2020.