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Title: High-fidelity human body modelling from user-generated data
Author: Xu, Zongyi
ISNI:       0000 0004 7962 5002
Awarding Body: Queen Mary, University of London
Current Institution: Queen Mary, University of London
Date of Award: 2019
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Building high-fidelity human body models for real people benefits a variety of applications, like fashion, health, entertainment, education and ergonomics applications. The goal of this thesis is to build visually plausible human body models from two kinds of user-generated data: low-quality point clouds and low-resolution 2D images. Due to the advances in 3D scanning technology and the growing availability of cost-effective 3D scanners to general users, a full human body scan can be easily acquired within two minutes. However, due to the imperfections of scanning devices, occlusion, self-occlusion and untrained scanning operation, the acquired scans tend to be full of noise, holes (missing data), outliers and distorted parts. In this thesis, the establishment of shape correspondences for human body meshes is firstly investigated. A robust and shape-aware approach is proposed to detect accurate shape correspondences for closed human body meshes. By investigating the vertex movements of 200 human body meshes, a robust non-rigid mesh registration method is proposed which combines the human body shape model with the traditional nonrigid ICP. To facilitate the development and benchmarking of registration methods on Kinect Fusion data, a dataset of user-generated scansis built, named Kinect-based 3D Human Body (K3D-hub) Dataset, with one Microsoft Kinect for XBOX 360. Besides building 3D human body models from point clouds, the problem is also tackled which estimates accurate 3D human body models from single 2D images. A state-of-the-art parametric 3D human body model SMPL is fitted to 2D joints as well as the boundary of the human body. Fast Region based CNN and deep CNN based methods are adopted to detect the 2D joints and boundary for each human body image automatically. Considering the commonly encountered scenario where people are in stable poses at most of the time, a stable pose prior is introduced from CMU motion capture (mocap) dataset for further improving the accuracy of pose estimation.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Human Body Modelling