Painterly rendering techniques: A state-of-the-art review of current approaches

In this publication we will look at the different methods presented over the past few decades which attempt to recreate digital paintings. While previous surveys concentrate on the broader subject of non-photorealistic rendering, the focus of this paper is firmly placed on painterly rendering techniques. We compare different methods used to produce different output painting styles such as abstract, colour pencil, watercolour, oriental, oil and pastel. Whereas some methods demand a high level of interaction using a skilled artist, others require simple parameters provided by a user with little or no artistic experience. Many methods attempt to provide more automation with the use of varying forms of reference data. This reference data can range from still photographs, video, 3D polygonal meshes or even 3D point clouds. The techniques presented here endeavour to provide tools and styles that are not traditionally available to an artist. Copyright © 2012 John Wiley & Sons, Ltd

A painterly approach to 3D graphics

This paper is a review and exploration of the work over the last ten years of Light Years Projects, a collaboration between Jeremy Gardiner and myself. It covers in particular how I have explored the notion of a painterly approach to real-time 3D computer graphics. It deals with the tensions, temptations and opportunities that lie in the area where landscape painting crosses with technology, concluding with some of the lessons I have learnt

Shading with Painterly Filtered Layers: A Process to Obtain Painterly Portraits

In this thesis, I study how color data from different styles of paintings can be extracted from photography with the end result maintaining the artistic integrity of the art style and having the look and feel of skin. My inspiration for this work came from the impasto style portraitures of painters such as Rembrandt and Greg Cartmell. I analyzed and studied the important visual characteristics of both Rembrandt’s and Cartmell’s styles of painting.These include how the artist develops shadow and shading, creates the illusion of subsurface scattering, and applies color to the canvas, which will be used as references to help develop the final renders in computer graphics. I also examined how color information can be extracted from portrait photography in order to gather accurate dark, medium, and light skin shades. Based on this analysis, I have developed a process for creating portrait paintings from 3D facial models. My process consists of four stages: (1) Modeling a 3D portrait of the subject, (2) data collection by photographing the subjects, (3) Barycentric shader development using photographs, and (4) Compositing with filtered layers. My contributions has been in stages (3) and (4) as follows: Development of an impasto-style Barycentric shader by extracting color information from gathered photographic images. This shader can result in realistic looking skin rendering. Development of a compositing technique that involves filtering layers of images that correspond to different effects such as diffuse, specular and ambient. To demonstrate proof-of-concept, I have created a few animations of the impasto style portrait painting for a single subject. For these animations, I have also sculpted high polygon count 3D model of the torso and head of my subject. Using my shading and compositing techniques, I have created rigid body animations that demonstrate the power of my techniques to obtain impasto style portraiture during animation under different lighting conditions

Deep Video Color Propagation

Traditional approaches for color propagation in videos rely on some form of matching between consecutive video frames. Using appearance descriptors, colors are then propagated both spatially and temporally. These methods, however, are computationally expensive and do not take advantage of semantic information of the scene. In this work we propose a deep learning framework for color propagation that combines a local strategy, to propagate colors frame-by-frame ensuring temporal stability, and a global strategy, using semantics for color propagation within a longer range. Our evaluation shows the superiority of our strategy over existing video and image color propagation methods as well as neural photo-realistic style transfer approaches.Comment: BMVC 201

Towards photo watercolorization with artistic verisimilitude

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