Teaching robots to paint in a manner similar to a human painter is an important task in computer vision. A recent paper on arXiv.org proposes a novel approach to this problem, which tackles several limitations of current algorithms.
Like in other methods, reinforcement learning is used to predict a sequence of brush strokes from a given image. However, instead of depicting one single image, the novel method employs a semantic guidance pipeline to learn the distinction between foreground and background brush strokes. Also, a neural alignment model is used to zoom in