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Generation of stroke-based non-photorealistic imagery, is an importantproblem in the computer vision community. As an endeavor in this direction,substantial recent research efforts have been focused on teaching machines "howto paint", in a manner similar to a human painter. However, the applicabilityof previous methods has been limited to datasets with little variation inposition, scale and saliency of the foreground object. As a consequence, wefind that these methods struggle to cover the granularity and diversitypossessed by real world images. To this end, we propose a Semantic Guidancepipeline with 1) a bi-level painting procedure for learning the distinctionbetween foreground and background brush strokes at training time. 2) We alsointroduce invariance to the position and scale of the foreground object througha neural alignment model, which combines object localization and spatialtransformer networks in an end to end manner, to zoom into a particularsemantic instance. 3) The distinguishing features of the in-focus object arethen amplified by maximizing a novel guided backpropagation based focus reward.The proposed agent does not require any supervision on human stroke-data andsuccessfully handles variations in foreground object attributes, thus,producing much higher quality canvases for the CUB-200 Birds and StanfordCars-196 datasets. Finally, we demonstrate the further efficacy of our methodon complex datasets with multiple foreground object instances by evaluating anextension of our method on the challenging Virtual-KITTI dataset. Source codeand models are available at https://github.com/1jsingh/semantic-guidance.

Combining Semantic Guidance and Deep Reinforcement Learning For Generating Human Level Paintings