Stroke Based Painterly Rendering with Mass Data through Auto Warping Generation

Painting is done according to the artist’s style.The most representative of the style is the texture and shape of the brush stroke.Computer simulations allow the artist’s painting to be produced by taking this stroke and pasting it onto the image.This is called stroke-based rendering.The quality of the result depends on the number or quality of this stroke,since the stroke is taken to create the image.It is not easy to render using a large amount of information,as there is a limit to having a stroke scanned.In this work,we intend to produce rendering results using mass data that produces large amounts of strokes by expanding existing strokes through warping.Through this,we have produced results that have higher quality than conventional studies.Finally,we also compare the correlation between the amount of data and the results.

NPRportrait 1.0:A three-level benchmark for non-photorealistic rendering of portraits

Recently,there has been an upsurge of activity in image-based non-photorealistic rendering(NPR),and in particular portrait image stylisation,due to the advent of neural style transfer(NST).However,the state of performance evaluation in this field is poor,especially compared to the norms in the computer vision and machine learning communities.Unfortunately,the task of evaluating image stylisation is thus far not well defined,since it involves subjective,perceptual,and aesthetic aspects.To make progress towards a solution,this paper proposes a new structured,threelevel,benchmark dataset for the evaluation of stylised portrait images.Rigorous criteria were used for its construction,and its consistency was validated by user studies.Moreover,a new methodology has been developed for evaluating portrait stylisation algorithms,which makes use of the different benchmark levels as well as annotations provided by user studies regarding the characteristics of the faces.We perform evaluation for a wide variety of image stylisation methods(both portrait-specific and general purpose,and also both traditional NPR approaches and NST)using the new benchmark dataset.

Automated pebble mosaic stylization of images

Digital mosaics have usually used regular tiles, simulating historical tessellated mosaics. In this paper, we present a method for synthesizing pebble mosaics, a historical mosaic style in which the tiles are rounded pebbles. We address both the tiling problem,of distributing pebbles over the image plane so as to approximate the input image content, and the problem of geometry, creating a smooth rounded shape for each pebble. We adopt simple linear iterative clustering(SLIC)to obtain elongated tiles conforming to image content,and smooth the resulting irregular shapes into shapes resembling pebble cross-sections. Then, we create an interior and exterior contour for each pebble and solve a Laplace equation over the region between them to obtain height-field geometry. The resulting pebble set approximates the input image while representing full geometry that can be rendered and textured for a highly detailed representation of a pebble mosaic.

Feature-preserving color pencil drawings from photographs

Color pencil drawing is well-loved due to its rich expressiveness.This paper proposes an approach for generating feature-preserving color pencil drawings from photographs.To mimic the tonal style of color pencil drawings,which are much lighter and have relatively lower saturation than photographs,we devise a lightness enhancement mapping and a saturation reduction mapping.The lightness mapping is a monotonically decreasing derivative function,which not only increases lightness but also preserves input photograph features.Color saturation is usually related to lightness,so we suppress the saturation dependent on lightness to yield a harmonious tone.Finally,two extremum operators are provided to generate a foreground-aware outline map in which the colors of the generated contours and the foreground object are consistent.Comprehensive experiments show that color pencil drawings generated by our method surpass existing methods in tone capture and feature preservation.