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Generative adversarial networks (GANs) can generate high-quality images fromsampled latent codes. Recent works attempt to edit an image by manipulating itsunderlying latent code, but rarely go beyond the basic task of attributeadjustment. We propose the first method that enables manipulation withmultidimensional condition such as keypoints and captions. Specifically, wedesign an algorithm that searches for a new latent code that satisfies thetarget condition based on the Surrogate Gradient Field (SGF) induced by anauxiliary mapping network. For quantitative comparison, we propose a metric toevaluate the disentanglement of manipulation methods. Thorough experimentalanalysis on the facial attribute adjustment task shows that our methodoutperforms state-of-the-art methods in disentanglement. We further apply ourmethod to tasks of various condition modalities to demonstrate that our methodcan alter complex image properties such as keypoints and captions.

Surrogate Gradient Field for Latent Space Manipulation