TY - GEN
T1 - Minimising Distortion for GAN-Based Facial Attribute Manipulation
AU - Shao, Mingyu
AU - Lu, Li
AU - Ding, Ye
AU - Liao, Qing
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Facial Attribute Manipulation (FAM) through GAN-based methods has been an active topic in computer graphics. Existing works show high editing fidelity on randomly generated faces but suffer from distortion on embedded real faces. We alleviate this issue by dividing it into two sub-problems. First, we minimize embedding distortion by introducing a pre-trained Salient Object Detection (SOD) network. Second, we propose a nonlinear transformation network to minimize editing distortion. As a result, our framework, Character Centered Facial Attribute Manipulation (CCFAM), exhibits more disentangled edits on real faces. Moreover, CCFAM is computationally efficient by integrating image complexity into our embedding process. Evaluations demonstrate that our method performs better than the state-of-the-art in terms of both accuracy and fidelity.
AB - Facial Attribute Manipulation (FAM) through GAN-based methods has been an active topic in computer graphics. Existing works show high editing fidelity on randomly generated faces but suffer from distortion on embedded real faces. We alleviate this issue by dividing it into two sub-problems. First, we minimize embedding distortion by introducing a pre-trained Salient Object Detection (SOD) network. Second, we propose a nonlinear transformation network to minimize editing distortion. As a result, our framework, Character Centered Facial Attribute Manipulation (CCFAM), exhibits more disentangled edits on real faces. Moreover, CCFAM is computationally efficient by integrating image complexity into our embedding process. Evaluations demonstrate that our method performs better than the state-of-the-art in terms of both accuracy and fidelity.
KW - Facial Attribute Manipulation
KW - GAN
KW - Salient Object Detection
UR - https://www.scopus.com/pages/publications/85177567694
U2 - 10.1109/ICASSP49357.2023.10095858
DO - 10.1109/ICASSP49357.2023.10095858
M3 - 会议稿件
AN - SCOPUS:85177567694
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
BT - ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023
Y2 - 4 June 2023 through 10 June 2023
ER -