3D reconstruction of human body via machine learning

Three-dimensional (3D) reconstruction and modeling of the human body and garments from images is a central open problem in computer vision, yet remains a challenge using machine learning techniques. We proposed a framework to generate the realistic 3D human from a single RGB image via machine learning. The framework is composed of an end-to-end 3D reconstruction neural net with a skinned multi-person linear model (SMPL) model by the generative adversarial networks (GANs). The 3D facial reconstruction used the morphable facial model by principal component analysis (PCA) and the LS3D-W database. The 3D garments are reconstructed by the multi-garment net (MGN) to generate UV-mapping and remapped into the human model with motion transferred by archive of motion capture as surface shapes (AMASS) dataset. The clothes simulated by the extended position based dynamics (XPBD) algorithm realized fast and realistic modeling.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Mechanical Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2020

Author dc:creator

• Hi, Qi,S.M.Massachusetts Institute of Technology.

Advisor dc:contributor.advisor

• Ju Li.

• Mechanical Engineering.

Statement dc:rights

• MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/127157

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/127157