Collaborative Learning for Faster StyleGAN Embedding

Abstract

The latent code of the recent popular model StyleGAN has learned disentangled representations thanks to the multi-layer style-based generator. Embedding a given image back to the latent space of StyleGAN enables wide interesting semantic image editing applications. Although previous works are able to yield impressive inversion results based on an optimization framework, which however suffers from the efficiency issue. In this work, we propose a novel collaborative learning framework that consists of an efficient embedding network and an optimization-based iterator. On one hand, with the progress of training, the embedding network gives a reasonable latent code initialization for the iterator. On the other hand, the updated latent code from the iterator in turn supervises the embedding network. In the end, high-quality latent code can be obtained efficiently with a single forward pass through our embedding network. Extensive experiments demonstrate the effectiveness and efficiency of our work.

10 pages, 11 figures

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Cited by in corpus (13)

• Face Generation and Editing with StyleGAN: A Survey
• Improved StyleGAN Embedding: Where are the Good Latents?
• Using latent space regression to analyze and leverage compositionality in GANs
• High-Fidelity GAN Inversion for Image Attribute Editing
• A Self Supervised StyleGAN for Image Annotation and Classification with Extremely Limited Labels
• HyperInverter: Improving StyleGAN Inversion via Hypernetwork
• GAN Inversion for Out-of-Range Images with Geometric Transformations
• Heredity-aware Child Face Image Generation with Latent Space Disentanglement
• Towards disease-aware image editing of chest X-rays
• Implicit Subspace Prior Learning for Dual-Blind Face Restoration
• One Shot Face Swapping on Megapixels
• StylePart: Image-based Shape Part Manipulation
• Multiple GAN Inversion for Exemplar-based Image-to-Image Translation