AgileGAN: stylizing portraits by inversion-consistent transfer learning

AgileGAN: stylizing portraits by inversion-consistent transfer learning

Portraiture as an art form has evolved from realistic depiction into a plethora of creative styles. While substantial progress has been made in automated stylization, generating high quality stylistic portraits is still a challenge, and even the recent popular Toonify suffers from several artifacts...

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Bibliographic Details
Main Authors: Song, Guoxian, Luo, Linjie, Liu, Jing, Ma, Wan-Chun, Lai, Chunpong, Zheng, Chuanxia, Cham, Tat-Jen
Other Authors: School of Computer Science and Engineering
Format: Journal Article
Language:English
Published: 2023
Subjects:
Engineering::Computer science and engineering::Computing methodologies::Computer graphics
Portrait Generation
Stylization
Online Access:https://hdl.handle.net/10356/172645

Internet

https://hdl.handle.net/10356/172645

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