  We introduce a novel image-to-painting method that facilitates the creation
of large-scale, high-fidelity paintings with human-like quality and stylistic
variation. To process large images and gain control over the painting process,
we introduce a segmentation-based painting process and a dynamic attention map
approach inspired by human painting strategies, allowing optimization of brush
strokes to proceed in batches over different image regions, thereby capturing
both large-scale structure and fine details, while also allowing stylistic
control over detail. Our optimized batch processing and patch-based loss
framework enable efficient handling of large canvases, ensuring our painted
outputs are both aesthetically compelling and functionally superior as compared
to previous methods, as confirmed by rigorous evaluations. Code available at:
https://github.com/manuelladron/semantic\_based\_painting.git
