Automatic three-dimensional-scanned garment fitting based on virtual tailoring and geometric sewing
Abstract
In this article, we presented a new automatic three-dimensional-scanned garment fitting method for A-Pose-scanned human models. Both the garment and the human body were decomposed based on feature lines defined by various landmarks. The patches of the three-dimensional garment were automatically positioned around the human model by setting up the correspondence via feature matching. Virtual sewing was engaged to obtain the final results of virtual dressing. The penetration between cloth model and human model was solved by a geometrical method constrained by Laplacian-based deformation. The experimental results indicated that the proposed method was an efficient way for redressing various garments onto various human models while maintaining the original geometrical features of garments.
Pengpeng Hu
Senior Lecturer (Associate Professor)
Pengpeng Hu is currently a Senior Lecturer (Associate Professor) with The University of Manchester. His research interests include biometrics, geometric deep learning, 3D human body reconstruction, point cloud processing, and vision-based measurement. He serves as an Associate Editor for IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Automation Science and Engineering, and Engineering and Mathematics in Medical and Life Sciences, as well as an Academic Editor for PLOS ONE and a member of the editorial board for Scientific Reports. He is also the Programme Chair for the 25th UK Workshop on Computational Intelligence (UKCI 2026) and an Area Chair for the 35th British Machine Vision Conference (BMVC 2024). He is the recipient of the Emerald Literati Award for an outstanding paper in 2019.