  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. Specifically, 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.
