Augmenting Hand-Drawn Art with Global Illumination Effects through Surface Inflation

We present a method for augmenting hand-drawn characters and creatures with global illumination effects. Given a single view drawing only, we use a novel CNN to predict a high-quality normal map of the same resolution. The predicted normals are then used as guide to inflate a surface into a 3D proxy mesh visually consistent and suitable to augment the input 2D art with convincing global illumination effects while keeping the hand-drawn look and feel. Along with this paper, a new high resolution dataset of line drawings with corresponding ground-truth normal and depth maps will be shared. We validate our CNN, comparing our neural predictions qualitatively and quantitatively with the recent state-of-the art, show results for various hand-drawn images and animations, and compare with alternative modeling approaches.