We present a fully automatic method to generate detailed and accurate artistic shadows from pairs of line drawing sketches and lighting directions. We also contribute a new dataset of one thousand examples of pairs of line drawings and shadows that are tagged with lighting directions. Remarkably, the generated shadows quickly communicate the underlying 3D structure of the sketched scene. Consequently, the shadows generated by our approach can be used directly or as an excellent starting point for artists. We demonstrate that the deep learning network we propose takes a hand-drawn sketch, builds a 3D model in latent space, and renders the resulting shadows. The generated shadows respect the hand-drawn lines and underlying 3D space and contain sophisticated and accurate details, such as self-shadowing effects. Moreover, the generated shadows contain artistic effects, such as rim lighting or halos appearing from back lighting, that would be achievable with traditional 3D rendering methods.
Samples from our dataset.
Our dataset comprises 1,160 sketch/shadow pairs and includes a variety of lighting directions and subjects. Specifically, 372 front-lighting, 506 side-lighting, 111 back-lighting, 85 center-back, and 86 center-front. With regard to subjects there are 867 single-person, 56 multi-person, 177 body-part, and 60 mecha.
More interactive tools (drag, rotate, scale, drawing panel, brush, eraser etc.).
Stylize our shadows with complex lighting directions, half-tone appearance, and compositing with colorized pictures.
Kabuki Actor Segawa Kikunojo III as the Shirabyoshi Hisakata Disguised as Yamato Manzai
Jardin de Paris
Coherent results learned from discrete tags.
Results from 26 discrete lighting directions.
Results in the wild.