Creating Digital Painting Lighting Effects via RGB-space Geometry and Deep Learning

Abstract

A way for generating illumination in a digital painting is represented. Two methods were approached for relighting pictures, first was a deep learning approach and the second was a traditional approach that is based on a critical observation. Painters paint lighting effects with multiple overlapping strokes; therefore, pixels with rich stroke historical past tend to accumulate more lighting strokes. Based on this identification, a technique was created that takes color geometry to assess the density of strokes in a painting, and then generates innovative lighting effects by replicating painters’ coarse- to-fine workflow. A wave transform is used to produce coarse illumination, which are subsequently retouched into usable lighting effects based on the stroke density of the source artwork. In addition, we urge artists to create lighting effects for comparison. Lighting artist, who is encouraged to paint lighting effects similar to the used approach’s results, and another non-lighting specialized artist, who is requested to do their best to relight the original image according to artistic vision. All results are then compared to evaluate the accuracy of the used approaches.