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Reverse Dissection and DiceCT Reveal Otherwise Hidden Data in the Evolution of the Primate Face
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InterFaceGAN: Interpreting the Disentangled Face Representation Learned by GANs
IEEE Transactions on Pattern Analysis and Machine Intelligence
|October 27, 2020
Summary
Generative adversarial networks (GANs) learn disentangled face representations in their latent space. This framework, InterFaceGAN, enables attribute manipulation and artifact correction without retraining GAN models.
Area of Science:
- Computer Vision
- Artificial Intelligence
Background:
- Generative adversarial networks (GANs) excel at photo-realistic image synthesis, particularly for faces.
- Understanding the latent space representation learned by GANs remains a challenge.
Purpose of the Study:
- To interpret the disentangled face representation learned by state-of-the-art GAN models.
- To study facial semantics encoded within the GAN latent space.
- To enable attribute manipulation and artifact correction in synthesized faces.
Main Methods:
- Proposing InterFaceGAN framework for latent space interpretation.
- Identifying linear subspaces corresponding to facial semantics.
- Utilizing subspace projection for disentangling attributes.
- Performing quantitative analysis of editing results.
Main Results:
- GANs learn facial semantics in linear subspaces of the latent space.
- Identified subspaces allow realistic manipulation of attributes like age, gender, expression, and pose.
- Improved disentanglement of semantics leads to precise attribute control.
- Approach successfully applied to real face editing via GAN inversion.
Conclusions:
- Synthesizing faces with GANs inherently learns a disentangled and controllable face representation.
- InterFaceGAN provides a method for interpreting and manipulating this learned representation.
- The framework facilitates attribute editing and artifact correction in GAN-generated faces.
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