Non-photorealistic Rendering with Cartesian Genetic Programming using Graphic Processing Units

Non-photorealistic rendering (NPR) is concerned with the algorithm generation of images having unrealistic characteristics, for example, oil paintings or watercolour. Using genetic programming to evolve aesthetically pleasing NPR images is a relatively new approach in the art field, and in the majority of cases it takes a lot of time to generate results. With use of Cartesian genetic programming (CGP) and graphic processing units (GPUs), we can improve the performance of NPR image evolution. Evolutionary NPR can render images with interesting, and often unexpected, graphic effects. CGP provides a means to eliminate large, inefficient rendering expressions, while GPU acceleration parallelizes the calculations, which minimizes the time needed to get results. By using these tools, we can speed up the image generation process. Experiments revealed that CGP expressions are more concise, and search is more exploratory, than in tree-based approaches. Implementation of the system with GPUs showed significant speed-up

Towards a human eye behavior model by applying Data Mining Techniques on Gaze Information from IEC

In this paper, we firstly present what is Interactive Evolutionary Computation (IEC) and rapidly how we have combined this artificial intelligence technique with an eye-tracker for visual optimization. Next, in order to correctly parameterize our application, we present results from applying data mining techniques on gaze information coming from experiments conducted on about 80 human individuals

Stroke Based Painterly Rendering

International audienceMany traditional art forms are produced by an artist sequentially placing a set of marks, such as brush strokes, on a canvas. Stroke based Rendering (SBR) is inspired by this process, and underpins many early and contemporary Artistic Stylization algorithms. This Chapter outlines the origins of SBR, and describes key algorithms for placement of brush strokes to create painterly renderings from source images. The chapter explores both local greedy, and global optimization based approaches to stroke placement. The issue of creative control in SBR is also briefly discussed

Mixed Media in Evolutionary Art

This thesis focuses on creating evolutionary art with genetic programming. The main goal of the system is to produce novel stylized images using mixed media. Mixed media on a canvas is the use of multiple artistic effects being used to produce interesting and new images. This approach uses a genetic program (GP) in which each individual in the population will represent their own unique solution. The evaluation method being used to determine the fitness of each individual will be direct colour matching of the GP canvas and target image. The secondary goal was to see how well different computer graphic techniques work together. In particular, bitmaps have not been studied much in evolutionary art. Results show a variety of unique solutions with the application of mixed media

AutoSimulate: (Quickly) Learning Synthetic Data Generation

Simulation is increasingly being used for generating large labelled datasets in many machine learning problems. Recent methods have focused on adjusting simulator parameters with the goal of maximising accuracy on a validation task, usually relying on REINFORCE-like gradient estimators. However these approaches are very expensive as they treat the entire data generation, model training, and validation pipeline as a black-box and require multiple costly objective evaluations at each iteration. We propose an efficient alternative for optimal synthetic data generation, based on a novel differentiable approximation of the objective. This allows us to optimize the simulator, which may be non-differentiable, requiring only one objective evaluation at each iteration with a little overhead. We demonstrate on a state-of-the-art photorealistic renderer that the proposed method finds the optimal data distribution faster (up to $50\times$), with significantly reduced training data generation (up to $30\times$) and better accuracy ($+8.7\%$) on real-world test datasets than previous methods.Comment: ECCV 202

Genetic Programming for Non-Photorealistic Rendering

This thesis focuses on developing an evolutionary art system using genetic programming. The main goal is to produce new forms of evolutionary art that filter existing images into new non-photorealistic (NPR) styles, by obtaining images that look like traditional media such as watercolor or pencil, as well as brand new effects. The approach permits GP to generate creative forms of NPR results. The GP language is extended with different techniques and methods inspired from NPR research such as colour mixing expressions, image processing filters and painting algorithm. Colour mixing is a major new contribution, as it enables many familiar and innovative NPR effects to arise. Another major innovation is that many GP functions process the canvas (rendered image), while is dynamically changing. Automatic fitness scoring uses aesthetic evaluation models and statistical analysis, and multi-objective fitness evaluation is used. Results showed a variety of NPR effects, as well as new, creative possibilities

Exploring a Parameterized Portrait Painting Space

We overview our interdisciplinary work building parameterized knowledge domains and their authoring tools that allow for expression systems which move through a space of painterly portraiture. With new computational systems it is possible to conceptually dance, compose and paint in higher level conceptual spaces. We are interested in building art systems that support exploring these spaces and in particular report on our software-based artistic toolkit and resulting experiments using parameter spaces in face based new media portraiture. This system allows us to parameterize the open cognitive and vision-based methodology that human artists have intuitively evolved over centuries into a domain toolkit to explore aesthetic realizations and interdisciplinary questions about the act of portrait painting as well as the general creative process. These experiments and questions can be explored by traditional and new media artists, art historians, cognitive scientists and other scholars