We present a novel pipeline to generate a depth map from a single image that can be used as input for a variety of artistic depth-based effects. In such a context, the depth maps do not have to be perfect but are rather designed with respect to a desired result. Consequently, our solution centers around user interaction and relies on a scribblebased depth editing. The annotations can be sparse,...

We present a study demonstrating how random walk algorithms can be used for evolutionary image transition. We design different mutation operators based on uniform and biased random walks and study how their combination with a baseline mutation operator can lead to interesting image transition processes in terms of visual effects and artistic features. Using feature-based analysis we investigate...

As a very creative research direction in the field of deep learning, style transfer has been attracted lot attention past two years, which can transform ordinary photos into artistic paintings and generate interesting pictures. Existing methods be divided categories: arbitrary single transfer. The representative method LapStyle synthesizes stylized image progressive procedure by using AdaIN, an...

We present an application of texture synthesis in creating collages. We use the texture synthesis method proposed by Ashikhmin [1] to selectively copy patches of the source image onto the image to be stylized, creating an automatic collage. The underlying algorithm is fast and allows the user to iteratively improve the quality of the resulting image. We show the use of this algorithm in capturi...