Adaptive Nonlinear Latent Transformation for Conditional Face Editing
Zhizhong Huang Siteng Ma Junping Zhang Hongming Shan
Recent works for face editing usually manipulate the latent space of StyleGAN
via the linear semantic directions. However, they usually suffer from the
entanglement of facial attributes, need to tune the optimal editing strength,
and are limited to binary attributes with strong supervision signals. This
paper proposes a novel adaptive nonlinear latent transformation for
disentangled and conditional face editing, termed AdaTrans. Specifically, our
AdaTrans divides the manipulation process into several finer steps; i.e., the
direction and size at each step are conditioned on both the facial attributes
and the latent codes. In this way, AdaTrans describes an adaptive nonlinear
transformation trajectory to manipulate the faces into target attributes while
keeping other attributes unchanged. Then, AdaTrans leverages a predefined
density model to constrain the learned trajectory in the distribution of latent
codes by maximizing the likelihood of transformed latent code. Moreover, we
also propose a disentangled learning strategy under a mutual information
framework to eliminate the entanglement among attributes, which can further
relax the need for labeled data. Consequently, AdaTrans enables a controllable
face editing with the advantages of disentanglement, flexibility with
non-binary attributes, and high fidelity. Extensive experimental results on
various facial attributes demonstrate the qualitative and quantitative
effectiveness of the proposed AdaTrans over existing state-of-the-art methods,
especially in the most challenging scenarios with a large age gap and few
labeled examples. The source code is available at
https://github.com/Hzzone/AdaTrans.