  Generative adversarial networks (GANs) can generate high-quality images from
sampled latent codes. Recent works attempt to edit an image by manipulating its
underlying latent code, but rarely go beyond the basic task of attribute
adjustment. We propose the first method that enables manipulation with
multidimensional condition such as keypoints and captions. Specifically, we
design an algorithm that searches for a new latent code that satisfies the
target condition based on the Surrogate Gradient Field (SGF) induced by an
auxiliary mapping network. For quantitative comparison, we propose a metric to
evaluate the disentanglement of manipulation methods. Thorough experimental
analysis on the facial attribute adjustment task shows that our method
outperforms state-of-the-art methods in disentanglement. We further apply our
method to tasks of various condition modalities to demonstrate that our method
can alter complex image properties such as keypoints and captions.
