Semantic Latent Decomposition with Normalizing Flows for Face Editing
Binglei Li Zhizhong Huang Hongming Shan Junping Zhang
Navigating in the latent space of StyleGAN has shown effectiveness for face
editing. However, the resulting methods usually encounter challenges in
complicated navigation due to the entanglement among different attributes in
the latent space. To address this issue, this paper proposes a novel framework,
termed SDFlow, with a semantic decomposition in original latent space using
continuous conditional normalizing flows. Specifically, SDFlow decomposes the
original latent code into different irrelevant variables by jointly optimizing
two components: (i) a semantic encoder to estimate semantic variables from
input faces and (ii) a flow-based transformation module to map the latent code
into a semantic-irrelevant variable in Gaussian distribution, conditioned on
the learned semantic variables. To eliminate the entanglement between
variables, we employ a disentangled learning strategy under a mutual
information framework, thereby providing precise manipulation controls.
Experimental results demonstrate that SDFlow outperforms existing
state-of-the-art face editing methods both qualitatively and quantitatively.
The source code is made available at https://github.com/phil329/SDFlow.