  We present a simple nearest-neighbor (NN) approach that synthesizes
high-frequency photorealistic images from an "incomplete" signal such as a
low-resolution image, a surface normal map, or edges. Current state-of-the-art
deep generative models designed for such conditional image synthesis lack two
important things: (1) they are unable to generate a large set of diverse
outputs, due to the mode collapse problem. (2) they are not interpretable,
making it difficult to control the synthesized output. We demonstrate that NN
approaches potentially address such limitations, but suffer in accuracy on
small datasets. We design a simple pipeline that combines the best of both
worlds: the first stage uses a convolutional neural network (CNN) to maps the
input to a (overly-smoothed) image, and the second stage uses a pixel-wise
nearest neighbor method to map the smoothed output to multiple high-quality,
high-frequency outputs in a controllable manner. We demonstrate our approach
for various input modalities, and for various domains ranging from human faces
to cats-and-dogs to shoes and handbags.
