Work

Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space?

Year: 2019

Type: conference-paper

Abstract: We propose an efficient algorithm to embed a given image into the latent space of StyleGAN. This embedding enables semantic image editing operations that can be applied to existing photographs. Taking the StyleGAN trained on the FFHD dataset as an example, we show results for image morphing, style transfer, and expression transfer. Studying the results of the embedding algorithm provides valuable insights into the structure of the StyleGAN latent space. We propose a set of experiments to test what class of images can be embedded, how they are embedded, what latent space is suitable for embedding, and if the embedding is semantically meaningful.

Source: IEEE/CVF International Conference on Computer Vision (ICCV)

DOI: 10.1109/iccv.2019.00453

Authors: Rameen Abdal, Yipeng Qin, Peter Wonka

Institution: King Abdullah University of Science and Technology

Language: English

Topic: Generative Adversarial Networks and Image Synthesis

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: King Abdullah University of Science and Technology

Award: G6997429520