Transforming and Projecting Images into
Class-conditional Generative Networks

Minyoung Huh12
Richard Zhang2
Jun-Yan Zhu2
Sylvain Paris2
Aaron Hertzmann2

2Adobe Research

Code [GitHub]

arXiv [Paper]


We present a method for projecting an input image into the space of a class-conditional generative neural network. We propose a method that optimizes for transformation to counteract the model biases in a generative neural networks. Specifically, we demonstrate that one can solve for image translation, scale, and global color transformation, during the projection optimization to address the object-center bias of a Generative Adversarial Network. This projection process poses a difficult optimization problem, and purely gradient-based optimizations fail to find good solutions. We describe a hybrid optimization strategy that finds good projections by estimating transformations and class parameters. We show the effectiveness of our method on real images and further demonstrate how the corresponding projections lead to better edit-ability of these images.




Results on BigGAN - ImageNet (256x256)

Results on StyleGAN2 - LSUN Cars (384x512)

Results on StyleGAN2 - FFHQ (1024x1024)


M. Huh, R. Zhang, JY. Zhu, S. Paris, A. Hertzmann,

Transforming and Projecting Images to
Class-conditional Generative Networks

In ECCV 2020 (oral) [arXiv]


We would like to thank Phillip Isola and David Bau for helpful discussions.

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