GAN Inverter

Last updated on Oct 31, 2023
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GAN Inverter
GAN GAN Inversion
Pu Cao
Pu Cao
Ph.D. student of Artificial Intelligence

I’m a second-year Ph.D. student studying at Beijing University of Posts and Telecommunications (BUPT) under the supervision of Prof. Qing Song and Dr. Lu Yang. I am now interested in Computer Vision and am currently working on Image Generaion.

Publications

What Decreases Editing Capability? Domain-Specific Hybrid Refinement for Improved GAN Inversion
Editing capability decreases ineivitably in previous refinement methods, (e.g., PTI, HFGI, and SAM). In this work, we explore the idea of “divide and conquer” to address this problem. We combine two mainstream refinement mechanisms (i.e., weight ande feature modulation) and achieve extroadinary inversion and editing results.
Pu Cao, Lu Yang, Dongxv Liu, Xiaoya Yang, Tianrui Huang, Qing Song
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What Decreases Editing Capability? Domain-Specific Hybrid Refinement for Improved GAN Inversion
LSAP: Rethinking Inversion Fidelity, Perception and Editability in GAN Latent Space
We analyse the resources of “Fidelity, Perception, and Editability” in inversion task and point out that the keypoint is disalignment between inverse latent codes and synthetic distribution. We then propose a simple but efficient and uniform solution in both optimization-based and encoder-based methods.
Pu Cao, Lu Yang, Dongxv Liu, Zhiwei Liu, Shan Li, Qing Song
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LSAP: Rethinking Inversion Fidelity, Perception and Editability in GAN Latent Space