It is a challenging task to teach machines to paint like human artists in a stroke-by-stroke fashion. Despite advances in stroke-based image rendering and deep learning-based image rendering, existing painting methods have limitations: they (i) lack flexibility to choose different art-style strokes, (ii) lose content details of images, and (iii) generate few artistic styles for paintings. In this paper, we propose a stroke-style generative adversarial network, called Stroke-GAN, to solve the first two limitations. Stroke-GAN learns styles of strokes from different stroke-style datasets, so can produce diverse stroke styles. We design three players in Stroke-GAN to generate pure-color strokes close to human artists’ strokes, thereby improving the quality of painted details. To overcome the third limitation, we have devised a neural network named Stroke-GAN Painter, based on Stroke-GAN; it can generate different artistic styles of paintings. Experiments demonstrate that our artful painter can generate various styles of paintings while well-preserving content details (such as details of human faces and building textures) and retaining high fidelity to the input images.

We propose an automatic video segmentation method based on an optimized SaliencyCut equipped with information centroid (IC) detection according to level balance principle in physical theory. Unlike the existing methods, the image information of another dimension is provided by the IC to enhance the video segmentation accuracy. Specifically, our IC is implemented based on the information-level balance principle in the image, and denoted as the information pivot by aggregating all the image information to a point. To effectively enhance the saliency value of the target object and suppress the background area, we also combine the color and the coordinate information of the image in calculating the local IC and the global IC in the image. Then saliency maps for all frames in the video are calculated based on the detected IC. By applying IC smoothing to enhance the optimized saliency detection, we can further correct the unsatisfied saliency maps, where sharp variations of colors or motions may exist in complex videos. Finally, we obtain the segmentation results based on IC-based saliency maps and optimized SaliencyCut. Our method is evaluated on the DAVIS dataset, consisting of different kinds of challenging videos. Comparisons with the state-of-the-art methods are also conducted to evaluate our method. Convincing visual results and statistical comparisons demonstrate its advantages and robustness for automatic video segmentation.