Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/158925
Title: Exploring deep learning methods for short-term skin texture simulation
Authors: Chen, Ziyu
Keywords: Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Chen, Z. (2022). Exploring deep learning methods for short-term skin texture simulation. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158925
Abstract: The skin plays a fundamental and significant role in our survival and body health. Also, our skin condition affects our mental health. Given the importance of skincare, it has attracted more and more interest in recent years and one of its significant parts is skin simulation, especially facial skin simulation. However, traditional methods require complicated modeling and lack robustness, and deep learning methods related to short-term facial skin texture simulation are yet to be explored. Hence it’s necessary to explore possible approaches to short-term facial skin texture simulation. In this dissertation, we explore several different deep learning methods, examine the viability of these approaches and give analysis. Moreover, we propose a possible approach: adopt U-Net to conduct pore segmentation and apply it to generator training.
URI: https://hdl.handle.net/10356/158925
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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