We present an algorithm to generate digital painting lighting effects from a single image. Our algorithm is based on a key observation: artists use many overlapping strokes to paint lighting effects, i.e., pixels with dense stroke history tend to gather more illumination strokes. Based on this observation, we design an algorithm to both estimate the density of strokes in a digital painting using color geometry, and then generate novel lighting effects by mimicking artists' coarse-to-fine workflow. Coarse lighting effects are first generated using a wave transform, and then retouched according to the stroke density of the original illustrations into usable lighting effects.
Our algorithm is content-aware, with generated lighting effects naturally adapting to image structures, and can be used as an interactive tool to simplify current labor-intensive workflows for generating lighting effects for digital and matte paintings. In addition, our algorithm can also produce usable lighting effects for photographs or 3D rendered images. We evaluate our approach with both an in-depth qualitative and a quantitative analysis which includes a perceptual user study. Results show that our proposed approach is not only able to produce favorable lighting effects with respect to existing approaches, but also that it is able to significantly reduce the needed interaction time.

@Article{ZhangTOG2020,
   author    = {Lvmin Zhang and Edgar Simo-Serra and Yi Ji and Chunping Liu},
   title     = {{Generating Digital Painting Lighting Effects via RGB-space Geometry}},
   journal   = "Transactions on Graphics (Presented at SIGGRAPH)",
   year      = 2020,
   volume    = 39,
   number    = 2,
}