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dc.contributor.authorChen, Ziyuen_US
dc.identifier.citationChen, Z. (2022). Exploring deep learning methods for short-term skin texture simulation. Master's thesis, Nanyang Technological University, Singapore.
dc.description.abstractThe 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.en_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Computer science and engineering::Computing methodologies::Image processing and computer visionen_US
dc.titleExploring deep learning methods for short-term skin texture simulationen_US
dc.typeThesis-Master by Courseworken_US
dc.contributor.supervisorAlex Chichung Koten_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeMaster of Science (Signal Processing)en_US
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