Learning to Shadow Hand-drawn Sketches
This provides a tool for artists to quickly add realistic shadows to sketches, though it is incremental as it builds on existing deep learning and 3D rendering techniques.
The paper tackles the problem of automatically generating artistic shadows from hand-drawn sketches and lighting directions, resulting in a method that produces detailed shadows with self-shadowing and artistic effects like rim lighting.
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.