Stroke-based Artistic Rendering Agent with Deep Reinforcement Learning

arXiv.org Artificial Intelligence

Excellent painters can use only a few strokes to create a fantastic painting, which is a symbol of human intelligence and art. Reversing the simulator to interpret images is also a challenging task of computer vision in recent years. In this paper, we present SARA, a stroke-based artistic rendering agent that combines the neural renderer and deep reinforcement learning (DRL), allowing the machine to learn the ability to deconstruct images using strokes and create amazing visual effects. Our agent is an end-to-end program that converts natural images into paintings. The training process does not require the experience of human painting or stroke tracking data.


hzwer/SARA_DDPG

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Excellent painters can use only a few strokes to create a fantastic painting, which is a symbol of human inte and art. Reversing the simulator to interpret images is also a challenging task of computer vision in recent years. In this paper, we propose a stroke-based rendering (SBR) method that combines the neural stroke renderer (NSR) and deep reinforcement learning (DRL), allowing the machine to learn the ability of deconstructing images using strokes and create amazing visual effects. Our agent is an end-to-end program that converts natural images into paintings. The training process does not require human painting experience or stroke tracking data.


Learning Oriental Ink Painting

AITopics Original Links

The oriental ink painting technique called Sumi-e is instantly recognizable no matter which part of the world you inhabit. The difference is that unlike western stroke based painting which uses layers of strokes to build up an image Sumi-e restricts its strokes in number - it is a minimalist approach to stroke based painting. This makes it very important to make maximum use of each stroke to convey what is to be seen. Thus Sumi-e strokes vary in thickness and style along the stroke. The appearance of the stroke is determined by the shape of an object to paint, the path and posture of the brush, and the distribution of pigments in the brush.


Stroke-based Character Recognition with Deep Reinforcement Learning

arXiv.org Machine Learning

The stroke sequence of characters is significant for the character recognition task. In this paper, we propose a stroke-based character recognition (SCR) method. We train a stroke inference module under deep reinforcement learning (DRL) framework. This module extracts the sequence of strokes from characters, which can be integrated with character recognizers to improve their robustness to noise. Our experiments show that the module can handle complicated noise and reconstruct the characters. Meanwhile, it can also help achieve great ability in defending adversarial attacks of character recognizers.


Creating better stroke treatment using AI and blockchain technology

#artificialintelligence

One in six people will suffer from stroke in their lifetime. Of the estimated 15 million victims worldwide, 6 million die every year and another 6 million are permanently disabled. Worse, the incidence of strokes is increasing, especially among people under age 55. By 2050, the number of strokes will have more than doubled, according to the American Stroke Association. Annual costs of stroke in the European Union alone are estimated at $53.1 billion.