Abstract
We study the problem of reconstructing an image from
information stored at contour locations. We show that highquality 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