  We study the problem of reconstructing an image from information stored at
contour locations. We show that high-quality reconstructions with high fidelity
to the source image can be obtained from sparse input, e.g., comprising less
than $6\%$ of image pixels. This is a significant improvement over existing
contour-based reconstruction methods that require much denser input to capture
subtle texture information and to ensure image quality. Our model, based on
generative adversarial networks, synthesizes texture and details in regions
where no input information is provided. The semantic knowledge encoded into our
model and the sparsity of the input allows to use contours as an intuitive
interface for semantically-aware image manipulation: local edits in contour
domain translate to long-range and coherent changes in pixel space. We can
perform complex structural changes such as changing facial expression by simple
edits of contours. Our experiments demonstrate that humans as well as a face
recognition system mostly cannot distinguish between our reconstructions and
the source images.
