In the last few years, artistic image-making with deep learning models has gained a considerable amount of traction. A large number of these models operate directly in the pixel space and generate raster images. This is however not how most humans would produce artworks, for example, by planning a sequence of shapes and strokes to draw. Recent developments in deep learning methods help to bridge the gap between stroke-based paintings and pixel photo generation. With this survey, we aim to provide a structured introduction and understanding of common challenges and approaches in stroke-based rendering algorithms. These algorithms range from simple rule-based heuristics to stroke optimization and deep reinforcement agents, trained to paint images with differentiable vector graphics and neural rendering.

Controllable image synthesis with user scribbles is a topic of keen interest in the computer vision community. In this paper, for the first time we study the problem of photorealistic image synthesis from incomplete and primitive human paintings. In particular, we propose a novel approach paint2pix, which learns to predict (and adapt) "what a user wants to draw" from rudimentary brushstroke inputs, by learning a mapping from the manifold of incomplete human paintings to their realistic renderings. When used in conjunction with recent works in autonomous painting agents, we show that paint2pix can be used for progressive image synthesis from scratch. During this process, paint2pix allows a novice user to progressively synthesize the desired image output, while requiring just few coarse user scribbles to accurately steer the trajectory of the synthesis process. Furthermore, we find that our approach also forms a surprisingly convenient approach for real image editing, and allows the user to perform a diverse range of custom fine-grained edits through the addition of only a few well-placed brushstrokes.

The generation of well-designed artwork is often quite time-consuming and assumes a high degree of proficiency on part of the human painter. In order to facilitate the human painting process, substantial research efforts have been made on teaching machines how to "paint like a human", and then using the trained agent as a painting assistant tool for human users. However, current research in this direction is often reliant on a progressive grid-based division strategy wherein the agent divides the overall image into successively finer grids, and then proceeds to paint each of them in parallel. This inevitably leads to artificial painting sequences which are not easily intelligible to human users. To address this, we propose a novel painting approach which learns to generate output canvases while exhibiting a more human-like painting style. The proposed painting pipeline Intelli-Paint consists of 1) a progressive layering strategy which allows the agent to first paint a natural background scene representation before adding in each of the foreground objects in a progressive fashion. 2) We also introduce a novel sequential brushstroke guidance strategy which helps the painting agent to shift its attention between different image regions in a semantic-aware manner. 3) Finally, we propose a brushstroke regularization strategy which allows for ~60-80% reduction in the total number of required brushstrokes without any perceivable differences in the quality of the generated canvases. Through both quantitative and qualitative results, we show that the resulting agents not only show enhanced efficiency in output canvas generation but also exhibit a more natural-looking painting style which would better assist human users express their ideas through digital artwork.

A team of scientists and art historians at Case Western Reserve University say they have used tools of artificial intelligence (AI) to distinguish the individual brushstrokes of one painter from another. The technique could become a valuable tool to help authorities better identify forgeries of work by famous artists; it could also help art historians tell whether a master, or a student, contributed to a given masterpiece. The researchers said they believe the finding is among first of its kind because of how the researchers used the computer to read and learn from the 3D topography of a painting. Other forms of AI-enhanced analysis rely on visible stylistic differences that a program may detect in historic works, they said. The technology of 3D topography describes a three-dimensional relief map of a surface which reveals any differences in "elevation."

Neural Painters is a class of models that follows a GAN framework to generate brushstrokes, which are then composed to create paintings. GANs are great generative models for AI Art but they are known to be notoriously difficult to train. To overcome GAN's limitations and to speed up the Neural Painter training, we applied Transfer Learning to the process reducing it from days to only hours, while achieving the same level of visual aesthetics in the final paintings generated. We report our approach and results in this work.

Summary: A painting of a naked woman by Picasso has been hidden under one of his "Blue Period" works for almost a century. With the help of artificial intelligence, researchers have been able to reproduce the lost painting. A painting of a naked woman by Pablo Picasso that has been hidden beneath one of his'Blue Period' masterpieces for more than a century, has been recreated by UCL scientists using a combination of X-rays, AI and 3D-printing. PhD researchers Anthony Bourached (UCL Queen Square Institute of Neurology) and George Cann (UCL Space and Climate Physics) have developed a five-step technology to reproduce art works, that have been painted over. For this, their third reproduction, they bought back to life the Spanish artist's depiction of a crouching nude woman; the painting was thought to have been lost until 2010 when X-rays revealed it lay behind The Blind Man's Meal. Dubbed'The Lonesome Crouching Nude', the image is also depicted as an unfinished painting in the background of Picasso's famous La Vie (The Life).

A painting of a naked woman by Pablo Picasso that has been hidden beneath one of his "Blue Period' masterpieces for more than a century has been recreated by UCL scientists using a combination of X-rays, AI and 3D printing. Ph.D. researchers Anthony Bourached (UCL Queen Square Institute of Neurology) and George Cann (UCL Space and Climate Physics) have developed a five-step technology to reproduce art works that have been painted over. For this, their third reproduction, they bought back to life the Spanish artist's depiction of a crouching nude woman; the painting was thought to have been lost until 2010 when X-rays revealed it lay behind "The Blind Man's Meal." Dubbed "The Lonesome Crouching Nude," the image is also depicted as an unfinished painting in the background of Picasso's famous "La Vie" (The Life). By using a combination of spectroscopic imaging, artificial intelligence, and 3D printing, the duo have created a full-size, full-color painting, which includes 3D textured brushstrokes. To help ensure the recreation was as close in look, feel and tone to the original, they developed an AI algorithm that analyzed dozens of Picasso's paintings and trained itself to understand the artist's style. Commenting, Bourached, who is researching Machine Learning and Behavioural Neuroscience at UCL, said, "We believe that Picasso likely painted over this piece with reluctance.

There have been many successful implementations of neural style transfer in recent years. In most of these works, the stylization process is confined to the pixel domain. However, we argue that this representation is unnatural because paintings usually consist of brushstrokes rather than pixels. We propose a method to stylize images by optimizing parameterized brushstrokes instead of pixels and further introduce a simple differentiable rendering mechanism. Our approach significantly improves visual quality and enables additional control over the stylization process such as controlling the flow of brushstrokes through user input. We provide qualitative and quantitative evaluations that show the efficacy of the proposed parameterized representation. Code is available at https:// github.

Human beings, who live by the day and make decisions instantly, have become increasingly dependent on algorithms during the pandemic as machines turn all their inclinations into predictions. Artist Refik Anadol has brought a different artistic interpretation to this addiction with his "Machine Memories: Space" art exhibition, which has recently opened in the Turkish metropolis of Istanbul. Anadol's solo exhibition has also attracted the great attention of giant technology companies around the world. The manager of the Massachusetts Institute of Technology (MIT) Media Lab, which I visited many years ago at the invitation of Turkey's leading information and communication technologies company Türk Telekom, described the success criteria as "making an impact." Now I have met Anadol, an artist who has created an impact all over the world and is admired by data companies.

A team of researchers from IBM Japan, the University of Tokyo and Yamaha Motors have created a robot that uses canvas, paint and a brush to create paintings on its own. What sets this artificial intelligence apart from some of the other artistically-inclined ones we've seen in the past is that it doesn't generate the paintings it creates at random. Instead, it's programmed to work with concepts and has a set of "values" it turns to for guidance. It's possible to shape the images it creates by providing it with additional instructions. Limit it to 30 or fewer brushstrokes and it will paint a more abstract piece. Conversely, with some 300 brushstrokes at its disposal, it will create something more realistic.