Aesthetics is the most fundal concept in Natural Computing; Aesthetics is knowledge itself and it can only halt computing in Nature. We redefine Computations and show that Aesthetics is irreducible concept in Natural Computing. We consider the Computational Aesthetics in visual by using generative arts. In order to investigate Computational Aesthetics in tactile sense, we propose Tactile Score and propose a design principle of Aesthetics of tactile sense based on investigation of massaging. The principle is verified by through examining biological responses of Stress marker and Brain activities when rationally designed tactile sense is given.
The depiction of motion in static representations has a long tradition in art and science alike. Often, motion is depicted by spatio-temporal summarizations that try to preserve as much information of the original dynamic content as possible. In our approach to depicting motion, we remove the spatial constraints and generate new content steered by the temporal changes in motion. Applying particle steering in combination with the dynamic color palette of the video content, we can create a wide range of different image styles. With recorded videos, or by live interaction with a webcam, one can influence the resulting image. We provide a set of intuitive parameters to affect the style of the result, the final image content depends on the video input. Based on a collection of results gathered from test users, we discuss example styles that can be achieved with FlowBrush. In general, our approach provides an open sandbox for creative people to generate aesthetic images from any video content they apply.
From a user interaction perspective, speech and sketching make a good couple for describing motion. Speech allows easy specification of content, events and relationships, while sketching brings in spatial expressiveness. Yet, we have insufficient knowledge of how sketching and speech can be used for motion-based video retrieval, because there are no existing retrieval systems that support such interaction. In this paper, we describe a Wizard-of-Oz protocol and a set of tools that we have developed to engage users in a sketch- and speech-based video retrieval task. We report how the tools and the protocol fit together using "retrieval of soccer videos" as a use case scenario. Our software is highly customizable, and our protocol is easy to follow. We believe that together they will serve as a convenient and powerful duo for studying a wide range of multi-modal use cases.
We present a drawing assistant for sketching and for assisting users in shading a hand drawn sketch. The augmented reality based system uses a sketch made by a professional and uses it to help inexperienced users to do sketching and shading. The input image is converted to a set of points based on simple heuristics for providing a "connect the dots" interface for a user to aid sketching. With the help of a 2.5D mesh generated by our algorithm, the system assists the user by providing information about the colors that can be given in different parts of the sketch. The system was tested with users of different age groups and skill levels, indicating its usefulness.
We propose a new taxonomy that explains the roles of motion in data visualization, focusing especially on their communicative aspects. Our taxonomy clarifies the main axis in how visualization designers can employ motion in data portrayal.
In this manuscript, we describe a process that can be used to create still and/or animated portrait paintings to be shown in Expressive Art Exhibit. Our process consists of two stages: (1) Creation of control textures for a Barycentric shader by using color information gathered from photographs to provide realistic looking skin rendering; (2) Filtering and compositing the layers of images that are obtained by control textures, which correspond to effects such as diffuse, specular and ambient. To demonstrate proof-of-concept, we have created a few rigid body animations of painterly portraits under different lighting conditions.
This article presents an easy to use mobile application which allows users to create 3D digital copies of their interested objects anywhere and anytime. An advanced 3-sweep modeling technique is developed to construct 3D primitives not only from generalized cylinder and cuboid, but also objects with symmetrical or non-uniformly scaled profiles. In addition, our system supports the texture and structure refinement which combine results created from multiple source images. The constructed 3D model will be the combination of our 3D primitives. The combined result can preserve more features which may not be seen from a single photo.
Abstraction in non-photorealistic rendering reduces the amount of detail, yet non-essential details can improve visual interest and thus make an image more appealing. In this paper, we propose an automatic system for photo manipulation that brightens an image and alters the detail levels. The process first applies an edge-preserving abstraction process to an input image, then uses the residual to reintroduce and exaggerate details in areas near strong edges. At the same time, image regions further from strong edges are brightened. The final result is a lively mixture of abstraction and enhanced detail.
Visual aesthetics is one of the fundamental perceptual properties of 3D shapes. Since the perception of shape aesthetics can be subjective, we take a data-driven approach and consider the human preferences of shape aesthetics. Previous work has considered a pairwise data collection approach, in which pairs of 3D shapes are shown to human participants and they are asked to choose one from each pair that they perceive to be more aesthetic. In this research, we study the question of whether the 3D modeling representation (e.g. polygon, points, or voxels) affects how people perceive the aesthetics of shape pairs. We find surprising results: for example the single-view and multi-view of shape pairs lead to similar user aesthetics choices; and a relatively low resolution of points or voxels is comparable to polygon meshes as they do not lead to significantly different user aesthetics choices. Our results has implications towards the data collection process of pairwise aesthetics data and the further use of such data in shape modeling problems.
Shadow art is a form of sculptural art in which the configuration of lights and sculptures cast 2D shadows for artistic effect. Previous computational methods for the creation of shadow art assumes a single point light that casts a bitonal shadow with sharp boundary. The goal of our study is to generate grayscale shadows using an area light (or an array of point lights) and multiple layers of occluder cells. The area light source casts soft shadows consisting of penumbra and umbra. The penumbra is the region in which only a portion of the light source is obscured by the occluders and the umbra is the region where the light sources are completely blocked by the occluders. The key challenge is to find the arrangement of the occluders such that each pixel in the shadow region gathers light from the partially occluded light source to yield a desired tone level. The problem can be formulated as combinatorial optimization with many binary variables. We present a stochastic algorithm that converges quickly to the target shadow image. Our algorithm generalizes easily to deal with arbitrary lighting and geometry setups. We demonstrate the potential of our system with a number of tonal images and the fabrication of artistic ornaments that cast grayscale shadows.
To ensure that all important moments of an event are represented and that challenging scenes are correctly captured, both amateur and professional photographers often opt for taking large quantities of photographs. As such, they are faced with the tedious task of organizing large collections and selecting the best images among similar variants. Automatic methods assisting with this task are based on independent assessment approaches, evaluating each image apart from other images in the collection. However, the overall quality of photo collections can largely vary due to user skills and other factors. In this work, we explore the possibility of context-aware image quality assessment, where the photo context is defined using a clustering approach, and statistics of both the extracted context and the entire photo collection are used to guide identification of low-quality photos. We demonstrate that our method is able to flexibly adapt to the nature of processed albums and to facilitate the task of image selection in diverse scenarios.
Decorative ornamentation involves a careful balance between accent and order. Existing techniques leave artists either with tedious manual processes or the uncontrolled automatic generation of rather homogeneous patterns that lack creatively-placed visual highlights. We present a method to close this gap, offering the control and quality of manual creation, and the efficiency and accuracy of computation. At the core of our system, customizable and modularly combinable element placement functions fill a space automatically under global design constraints. We provide a set of example placement functions that implement order based on design principles for ornamentation such as balanced element distribution and symmetry. To create structural hierarchies and to guide an ornament to the space it fills, we allow artists to direct the connectivity of elements with drawn strokes. Artists can also draw guides to create vector fields, which organize the ornament along streamlines. Path planning automatically routes around obstacles while aligning the ornament to their borders. Our method combines high-level control mechanisms like taking guidance from example images to low-level control like placing single elements as visual accents and making local edits within the computed ornament. By automating tedious tasks and offering familiar input mechanisms like drawing, we enable artists to focus on the creative intent.
Art aspires to surprise an observer and to offer a different perspective. Changing one's perspective enables a deeper understanding of the examined subject and gives insights that are invisible in the original. We propose a method to automatically deconstruct an image into visually coherent constituents and to rearrange those pieces in a surprising, aesthetically pleasing, and potentially informative fashion. Our pipeline is flexible and users can create their individual desired artistic expressions. We show with a survey that the visual appeal of the results vary in regard to the chosen parameter combinations. Lastly, we showcase a variety of examples that explore the design space and hope to show that a reconfiguration in itself presents a new piece of art.
In this paper we describe a system aimed at the generation and analysis of graffiti tags. We argue that the dynamics of the movement involved in generating tags is in large part --- and at a higher degree with respect to many other visual art forms --- determinant of their stylistic quality. To capture this notion computationally, we rely on a biophysically plausible model of handwriting gestures (the Sigma Lognormal Model proposed by Réjean Plamondon et al.) that permits the generation of curves which are aesthetically and kinetically similar to the ones made by a human hand when writing. We build upon this model and extend it in order to facilitate the interactive construction and manipulation of digital tags. We then describe a method that reconstructs any planar curve or a sequence of planar points with a set of corresponding model parameters. By doing so, we seek to recover plausible velocity and temporal information for a static trace. We present a number of applications of our system: (i) the interactive design of curves that closely resemble the ones typically observed in graffiti art; (ii) the stylisation and beautification of input point sequences via curves that evoke a smooth and rapidly executed movement; (iii) the generation of multiple instances of a synthetic tag from a single example. This last application is a step in the direction of our longer term plan of realising a system which is capable of automatically generating convincing images in the graffiti style space.
Space filling curves, invented by mathematicians in the 19th century, have long been a fascination for artists, however there are no interactive tools to allow an artist to create and explore various levels of recursion of the curve in different parts of the artwork. In this work a new type of painting tool for artists is introduced, which gives the artist control over the very base of a space filling curve, i.e recursive subdivision. Although there are many such curves that would lend themselves to this treatment, the Flowsnake (Gosper) curve has been chosen in this work, mainly for its aesthetics. The curve is based on a hexagonal grid, and in our system hexagons are subdivided at the artist's touch in a non-homogeneous manner, leaving a trail that forms the space filling curve. Some tools are introduced for controlling the painting, such as limiting the depth of recursion, and the 'slow brush', which interpolates slowly between subdivisions to allow the artist to stop at a chosen level. A set of space filling curve brush types provide different shapes and profiles, for giving the artist control of the non-homogeneous subdivision, including the ability to un-subdivide the hexagons. An algorithm for drawing the curve non-recursively is introduced in order to produce a polyline suitable for processing on the GPU to make the system function at interactive rates. An animated version of the image can be made by replaying the subdivisions from the first level. Some examples made by art students and graduates are shown, along with the artist's comments on the system.
We propose a two-stage approach to painterly rendering of photographs, where the image plane is first warped to produce a distorted or caricatured effect and then the resulting image is rendered with a painterly effect. We use SLIC superpixels to obtain an oversegmentation, and assign spring parameters uniformly to all pixels within a region; then, the mass-spring simulation distorts the plane in a random but content-sensitive way. With aggressive warping, the subsequent painterly rendering can be done lightly and need not remove much detail. The resulting renderings convey a sense of being painted and leave a sense of being handmade and not overly beholden to the photographic scene.
This paper introduces a technique that enables the creative reshaping of one or more video signals based on granular synthesis techniques, normally applied only to audio signals. We demonstrate that a wide range of novel video processing effects can be generated through conceptualizing a video signal as being composed of a large number of video grains. These grains can be manipulated and maneuvered in a variety of ways, and a new video signal can then be created through the resynthesis of these altered grains; effects include cloning, rotating, and resizing the video grains, as well as repositioning them in space and time. These effects have been used successfully in a series of interactive multimedia performances, leading us to believe that our approach has significant artistic potential.
Transferring features, such as light and colors, between input and reference images is the main objective of color transfer methods. Current state-of-the-art methods focus mainly on the complete transfer of the light and color distributions. However, they do not successfully grasp specific light and color variations in image styles. In this paper, we propose a local method for carrying out a transfer of style between two images. Our method partitions both images to Gaussian distributed clusters by considering their main style features. These features are automatically determined by the classification step of our algorithm. Moreover, we present several novel policies for input/reference cluster mapping, which have not been tackled so far by previous methods. To complete the style transfer, for each pair of corresponding clusters, we apply a parametric color transfer method and a local chromatic adaptation transform. Results, subjective user evaluation as well as objective evaluation show that the proposed method obtains visually pleasing and artifact-free images, respecting the reference style.
We describe an image-based non-photorealistic rendering pipeline for creating portraits in two styles: The first is a somewhat "puppet" like rendering, that treats the face like a relatively uniform smooth surface, with the geometry being emphasised by shading. The second style is inspired by the artist Julian Opie, in which the human face is reduced to its essentials, i.e. homogeneous skin, thick black lines, and facial features such as eyes and the nose represented in a cartoon manner. Our method is able to automatically generate these stylisations without requiring the input images to be tightly cropped, direct frontal view, and moreover perform abstraction while maintaining the distinctiveness of the portraits (i.e. they should remain recognisable).
This paper presents an approach for transforming images into an oil paint look. To this end, a color quantization scheme is proposed that performs feature-aware recolorization using the dominant colors of the input image. In addition, an approach for real-time computation of paint textures is presented that builds on the smoothed structure adapted to the main feature contours of the quantized image. Our stylization technique leads to homogeneous outputs in the color domain and enables creative control over the visual output, such as color adjustments and per-pixel parametrizations by means of interactive painting.
