Synthetic environment for machine learning experiments
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Mithun Lal Thesis.pdf. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. |
Description
This thesis addresses the problem of data scarcity in human deep-learning applications. Automated estimation of human shape and pose from an image is challenging. It is even more difficult to map the identified human pixels onto a 3D model. Existing deep-learning models learn to map manually labelled human pixels in 2D images onto human surface, which is prone to human error, and the sparsity of annotated data leads to sub-optimal results. We solve this problem by generating realistic artificial human video data to train 2D-3D human mapping models and show promising results when compared to models trained on real data.
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| ID Code: | 236035 |
|---|---|
| Item Type: | QUT Thesis (Master of Philosophy) |
| Supervisor: | Fookes, Clinton & Paproki, Anthony |
| Keywords: | Human Modelling, 2D-3D Mapping, Dense Correspondence, Simulation, Machine Learning, Neural Networks, Geometric Modelling, Synthetic Data, Densepose, 3D Motion Capture |
| DOI: | 10.5204/thesis.eprints.236035 |
| Pure ID: | 117116611 |
| Divisions: | Current > QUT Faculties and Divisions > Faculty of Engineering Current > Schools > School of Electrical Engineering & Robotics |
| Institution: | Queensland University of Technology |
| Deposited On: | 04 Nov 2022 05:44 |
| Last Modified: | 25 Jan 2023 02:49 |
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