1,908 research outputs found

    Exploring the structure of a real-time, arbitrary neural artistic stylization network

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    In this paper, we present a method which combines the flexibility of the neural algorithm of artistic style with the speed of fast style transfer networks to allow real-time stylization using any content/style image pair. We build upon recent work leveraging conditional instance normalization for multi-style transfer networks by learning to predict the conditional instance normalization parameters directly from a style image. The model is successfully trained on a corpus of roughly 80,000 paintings and is able to generalize to paintings previously unobserved. We demonstrate that the learned embedding space is smooth and contains a rich structure and organizes semantic information associated with paintings in an entirely unsupervised manner.Comment: Accepted as an oral presentation at British Machine Vision Conference (BMVC) 201

    Arrangement and Timing: Photography, Causation and Anti-Empiricist Aesthetics

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    According to the causal theory of photography (CTP), photographs acquire their depictive content from the world, whereas handmade pictures acquire their depictive content from their makers’ intentional states about the world. CTP suffers from what I call the Problem of the Missing Agent: it seemingly leaves no room for the photographer to occupy a causal role in the production of their pictures and so is inconsistent with an aesthetics of photography. In this paper, I do three things. First, I amend CTP with Fred Dretske’s distinction between triggering and structuring causes, thereby overcoming the Problem of the Missing Agent. Second, I argue that CTP so amended in fact illuminates two aesthetic interests that we may take in photographs, focussing on photographic portraiture and street photography. Third, I show how reflection on the aesthetics of photography serves to support aesthetic anti-empiricism: the view that the aesthetic value of artworks consists, at least in part, in achievement rather than sensory pleasure

    Perceptual 3D rendering based on principles of analytical cubism

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    Cataloged from PDF version of article.Cubism, pioneered by Pablo Picasso and Georges Braque, was a breakthrough in art, influencing artists to abandon existing traditions. In this paper, we present a novel approach for cubist rendering of 3D synthetic environments. Rather than merely imitating cubist paintings, we apply the main principles of analytical cubism to 3D graphics rendering. In this respect, we develop a new cubist camera providing an extended view, and a perceptually based spatial imprecision technique that keeps the important regions of the scene within a certain area of the output. Additionally, several methods to provide a painterly style are applied. We demonstrate the effectiveness of our extending view method by comparing the visible face counts in the images rendered by the cubist camera model and the traditional perspective camera. Besides, we give an overall discussion of final results and apply user tests in which users compare our results very well with analytical cubist paintings but not synthetic cubist paintings. (c) 2012 Elsevier Ltd. All rights reserved

    Algorithms for sketching surfaces

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    CISRG discussion paper ; 1

    A Neural Algorithm of Artistic Style

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    In fine art, especially painting, humans have mastered the skill to create unique visual experiences through composing a complex interplay between the content and style of an image. Thus far the algorithmic basis of this process is unknown and there exists no artificial system with similar capabilities. However, in other key areas of visual perception such as object and face recognition near-human performance was recently demonstrated by a class of biologically inspired vision models called Deep Neural Networks. Here we introduce an artificial system based on a Deep Neural Network that creates artistic images of high perceptual quality. The system uses neural representations to separate and recombine content and style of arbitrary images, providing a neural algorithm for the creation of artistic images. Moreover, in light of the striking similarities between performance-optimised artificial neural networks and biological vision, our work offers a path forward to an algorithmic understanding of how humans create and perceive artistic imagery

    Stroke Based Painterly Rendering

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    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
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