The lack of information provided by line arts makes user guided-colorization a challenging task for computer vision. Recent contributions from the deep learning community based on Generative Adversarial Network (GAN) have shown incredible results compared to previous techniques. These methods employ user input color hints as a way to condition the network. The current state of the art has shown the ability to generalize and generate realistic and precise colorization by introducing a custom dataset and a new model with its training pipeline. Nevertheless, their approach relies on randomly sampled pixels as color hints for training. Thus, in this contribution, we introduce a stroke simulation based approach for hint generation, making the model more robust to messy inputs. We also propose a new cleaner dataset, and explore the use of a double generator GAN to improve visual fidelity.
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