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ORCID
https://orcid.org/0000-0003-2243-0725
Access Type
Open Access Thesis
Document Type
thesis
Degree Program
Electrical & Computer Engineering
Degree Type
Master of Science in Electrical and Computer Engineering (M.S.E.C.E.)
Year Degree Awarded
2022
Month Degree Awarded
September
Abstract
Sketch-to-photo synthesis usually faced the problem of lack of labeled data, so we propose some methods based on CycleGAN to train a model to translate sketch to photo with unpaired data. Our main contribution is a proposed Sketch-to-Skeleton-to-Image (SSI) method, which performs skeletonization on sketches to reduce variance on the sketch data. We also tried different representations of the skeleton and different models for our task. Experiment results show that the generated image quality has a negative correlation with the sparsity of the input data.
DOI
https://doi.org/10.7275/30957864
First Advisor
Marco F. Duarte
Recommended Citation
Gu, Yuanzhe, "Unpaired Skeleton-to-Photo Translation for Sketch-to-Photo Synthesis" (2022). Masters Theses. 1242.
https://doi.org/10.7275/30957864
https://scholarworks.umass.edu/masters_theses_2/1242