Abstract
In this Letter, the authors propose a deep learning based method to perform semantic segmentation of clothes from RGB-D images of people. First, they present a synthetic dataset containing more than 50,000 RGB-D samples of characters in different clothing styles, featuring various poses and environments for a total of nine semantic classes. The proposed data generation pipeline allows for fast production of RGB, depth images and ground-truth label maps. Secondly, a novel multi-modal encoder–ecoder convolutional network is proposed which operates on RGB and depth modalities. Multi-modal features are merged using trained fusion modules which use multi-scale atrous convolutions in the fusion process. The method is numerically evaluated on synthetic data and visually assessed on real-world data. The experiments demonstrate the efficiency of the proposed model over existing methods.
Pengpeng Hu
Assistant Professor
Pengpeng Hu is currently an Assistant Professor with the Center for Computational Science and Mathematical Modeling, Coventry University, Coventry, U.K. He was a Senior Researcher with the Department of Electronics and Informatics, Vrije Universiteit Brussel (VUB), Brussels, Belgium. In 2016, he was a Visiting Scholar with the School of Informatics, Edinburgh University, Edinburgh, U.K. In 2017, he was a Post-Doctoral Fellow with the Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, U.K. Since 2018, he has been with VUB. His current research interests include biometrics, geometric deep learning, 3-D human body reconstruction, point cloud processing, and measurement.