Recent advances like StyleGAN have promoted the growth of controllable facial editing. To address its core challenge of attribute decoupling in a single latent space, attempts have been made to adopt dual-space GAN for better disentanglement of style and content representations. Nonetheless, these methods are still incompetent to obtain plausible editing results with high controllability, especially for complicated attributes. In this study, we highlight the importance of interaction in a dual-space GAN for more controllable editing. We propose TransEditor, a novel Transformer-based framework to enhance such interaction. Besides, we develop a new dual-space editing and inversion strategy to provide additional editing flexibility. Extensive experiments demonstrate the superiority of the proposed framework in image quality and editing capability, suggesting the effectiveness of TransEditor for highly controllable facial editing.
Paper
Yanbo Xu*, Yueqin Yin*, Liming Jiang, Qianyi Wu, Chengyao Zheng, Chen Change Loy, Bo Dai, Wayne Wu. TransEditor: Transformer-Based Dual-Space GAN for Highly Controllable Facial Editing . In CVPR, 2022. (Paper)
Two latent spaces Z and P are used for generation. We correlate them via a cross-attention-based interaction module to facilitate editing.
Interpolation of two latent spaces. They are disentangled with different semantic meanings.
Interpolating Z space
Interpolating P space
Editing Results
Smile editing on Z space
Gender editing on Z and P space
Head pose editing on P space
Age editing on Z and P space
Comparison
Our method shows better editing ability compared with other SOTA methods.
Gender Editing Comparison
Pose Editing Comparison
Acknowledgements
This study is partly supported under the RIE2020 Industry Alignment Fund Industry Collaboration Projects (IAF-ICP) Funding Initiative, as well as cash and in-kind contribution from the industry partner(s).
