Transforming and Projecting Images into Class-conditional Generative Networks
This work addresses the challenge of aligning real images with generative models for better manipulation, but it is incremental as it builds on existing projection methods.
The paper tackles the problem of projecting real images into class-conditional generative networks by optimizing transformations to counteract model biases like object-center and color bias, resulting in improved editability of images.
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 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 and color 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 editability of these images.