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Which Style Makes Me Attractive? Interpretable Control Discovery and Counterfactual Explanation on StyleGAN
The semantically disentangled latent subspace in GAN provides rich interpretable controls in image generation. This paper includes two contributions on semantic latent subspace analysis in the scenario of face generation using StyleGAN2. First, we propose a novel approach to disentangle latent subspace semantics by exploiting existing face analysis models, e.g., face parsers and face landmark detectors. These models provide the flexibility to construct various criterions with very concrete anddoi:10.48550/arxiv.2201.09689 fatcat:cj4gby3zkjdgdmnrw3z43vfqge