We study the problem of reconstructing an image from information stored at sparse contour locations comprising less than $6\%$ of image pixels. This extremely sparse representation provides an intuitive interface for semantically-aware image manipulation. Local edits in contour domain translate to long-range and coherent changes in pixel space. We use generative adversarial networks to synthesize texture and structure even in regions where no input information is provided. With our setup, we can perform complex structural changes such as changing facial expression and interpolating animal fur texture by simple edits of contours such as scaling, moving and erasing. Experiments on a variety of datasets verify the versatility and convenience afforded by our models.