2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDARS-5.4
Paper Title LEARNING NON-LINEAR DISENTANGLED EDITING FOR STYLEGAN
Authors Xu Yao, Alasdair Newson, Yann Gousseau, Telecom Paris, France; Pierre Hellier, Interdigital, France
SessionARS-5: Image and Video Synthesis, Rendering and Visualization
LocationArea I
Session Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Poster
Topic Image and Video Analysis, Synthesis, and Retrieval: Image & Video Synthesis, Rendering, and Visualization
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Recent work has demonstrated the great potential of image editing in the latent space of powerful deep generative models such as StyleGAN. However, the success of such methods relies on the assumption that a linear hyperplane may separate the latent space into two subspaces for a binary attribute. In this work, we show that this hypothesis is a significant limitation and propose to learn a non-linear, regularized and identity-preserving latent space transformation that leads to more accurate and disentangled manipulations of facial attributes.