Master Assignment
[B] Disentangled Flows for Face editing
Type: Master EE/CS/ITC
Period: TBD
Student: (Unassigned)
If you are interested please contact :
Background:
Disentangled Flows for Face editing
Face editing can take place by navigating the latent space of StyleGAN, however it faces challenges in the case of complicated navigation due to the entanglement of latent factors. SDFlow has been proposed, for the semantic decomposition in original latent space using continuous conditional normalizing flows. SDFlow jointly optimizes: (i) a semantic encoder to estimate semantic variables from input faces and (ii) a flow-based trans formation module to map the latent code into a semantic-irrelevant variable in Gaussian distribution, conditioned on the learned semantic variables. A disentangled learning strategy under a mutual information framework is used, allowing for precise manipulation and SoA face editing. This thesis will examine the effects face editing can achieve and implement explainability techniques to understand the effect of disentangled latent spaces.
- Disentangled Flows for Face editing https://arxiv.org/pdf/2309.05314.pdf