Face Attribute Editing with Disentangled Latent Vectors

• URL: http://arxiv.org/abs/2301.04628v1
• Date: Wed, 11 Jan 2023 18:32:13 GMT
• Title: Face Attribute Editing with Disentangled Latent Vectors
• Authors: Yusuf Dalva, Hamza Pehlivan, Cansu Moran, \"Oyk\"u Irmak Hatipo\u{g}lu, Ay\c{s}eg\"ul D\"undar
• Abstract summary: We propose an image-to-image translation framework for facial attribute editing. Inspired by the latent space factorization works of fixed pretrained GANs, we design the attribute editing by latent space factorization. To project images to semantically organized latent spaces, we set an encoder-decoder architecture with attention-based skip connections.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose an image-to-image translation framework for facial attribute editing with disentangled interpretable latent directions. Facial attribute editing task faces the challenges of targeted attribute editing with controllable strength and disentanglement in the representations of attributes to preserve the other attributes during edits. For this goal, inspired by the latent space factorization works of fixed pretrained GANs, we design the attribute editing by latent space factorization, and for each attribute, we learn a linear direction that is orthogonal to the others. We train these directions with orthogonality constraints and disentanglement losses. To project images to semantically organized latent spaces, we set an encoder-decoder architecture with attention-based skip connections. We extensively compare with previous image translation algorithms and editing with pretrained GAN works. Our extensive experiments show that our method significantly improves over the state-of-the-arts. Project page: https://yusufdalva.github.io/vecgan

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