This is the project site of the High-Fidelity and Arbitrary Face Editing.


Paper: arXiv
Dataset: CelebaHQ | FFHQ


Cycle consistency is widely used for face editing. However, we observe that the generator tends to find a tricky way to hide information from the original image to satisfy the constraint of cycle consistency, making it impossible to maintain the rich details (e.g., wrinkles and moles) of non-editing areas. In this work, we propose a simple yet effective method named HifaFace to address the above-mentioned problem from two perspectives. First, we relieve the pressure of the generator to synthesize rich details by directly feeding the high-frequency information of the input image into the end of the generator. Second, we adopt an additional discriminator to encourage the generator to synthesize rich details. Speci´Čücally, we apply wavelet transformation to transform the image into multi-frequency domains, among which the high-frequency parts can be used to recover the rich details. We also notice that a fine-grained and wider-range control for the attribute is of great importance for face editing. To achieve this goal, we propose a novel attribute regression loss. Powered by the proposed framework, we achieve high-fidelity and arbitrary face editing, outperforming other state-of-the-art approaches.


Cycle consistency in CycleGANs causes steganography. The key idea of our method is to adopt a wavelet-based generator and a high-frequency discriminator.



Model Architecture


Main Results

Attribute-Based Face Editing


Arbitrary Face Editing



If you find our work is helpful, please consider citing our work:

  title={High-Fidelity and Arbitrary Face Editing},
  author={Gao, Yue and Wei, Fangyun and Bao, Jianmin and Gu, 
          Shuyang and Chen, Dong and Wen, Fang and Lian, Zhouhui},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},