Graphics, Imaging, and Games Lab

The non-photorealistic rendering community has had difficulty evaluating its research results. Other areas of computer graphics, and related disciplines such as computer vision, have made progress by comparing algorithms' performance on common datasets, or benchmarks. We argue for the benefits of establishing a benchmark image set to which image stylization methods can be applied, making comparisons between methods simpler, and encouraging more thorough testing of individual methods. We propose a preliminary set of benchmark images, chosen to represent a range of possible subject matter and image features of interest to researchers, and we describe the policies, tradeoffs, and reasoning that led us to the particular images in the set.