AgileGAN: stylizing portraits by inversion-consistent transfer learning

• 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: 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
• Institution: Nanyang Technological University

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...