3D reconstruction of human body via machine learning
Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, May, 2020
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2020
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| Online Access: | https://hdl.handle.net/1721.1/127157 |
| _version_ | 1811096595688587264 |
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| author | Hi, Qi,S.M.Massachusetts Institute of Technology. |
| author2 | Ju Li. |
| author_facet | Ju Li. Hi, Qi,S.M.Massachusetts Institute of Technology. |
| author_sort | Hi, Qi,S.M.Massachusetts Institute of Technology. |
| collection | MIT |
| description | Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, May, 2020 |
| first_indexed | 2024-09-23T16:46:11Z |
| format | Thesis |
| id | mit-1721.1/127157 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T16:46:11Z |
| publishDate | 2020 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1271572020-09-04T03:01:38Z 3D reconstruction of human body via machine learning 3 dimensional reconstruction of human body via machine learning Three-dimensional reconstruction of human body via machine learning Hi, Qi,S.M.Massachusetts Institute of Technology. Ju Li. Massachusetts Institute of Technology. Department of Mechanical Engineering. Massachusetts Institute of Technology. Department of Mechanical Engineering Mechanical Engineering. Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, May, 2020 Cataloged from the official PDF of thesis. Includes bibliographical references (pages 55-59). 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. by Qi He. S.M. S.M. Massachusetts Institute of Technology, Department of Mechanical Engineering 2020-09-03T17:49:51Z 2020-09-03T17:49:51Z 2020 2020 Thesis https://hdl.handle.net/1721.1/127157 1191844129 eng 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. http://dspace.mit.edu/handle/1721.1/7582 59 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Mechanical Engineering. Hi, Qi,S.M.Massachusetts Institute of Technology. 3D reconstruction of human body via machine learning |
| title | 3D reconstruction of human body via machine learning |
| title_full | 3D reconstruction of human body via machine learning |
| title_fullStr | 3D reconstruction of human body via machine learning |
| title_full_unstemmed | 3D reconstruction of human body via machine learning |
| title_short | 3D reconstruction of human body via machine learning |
| title_sort | 3d reconstruction of human body via machine learning |
| topic | Mechanical Engineering. |
| url | https://hdl.handle.net/1721.1/127157 |
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