We propose a high-quality photo-to-pencil translation method with fine-grained control over the drawing style. This is a challenging task due to multiple stroke types (e.g., outline and shading), structural complexity of pencil shading (e.g., hatching), and the lack of aligned training data pairs. To address these challenges, we develop a two-branch model that learns separate filters for generating sketchy outlines and tonal shading from a collection of pencil drawings. We create training data pairs by extracting clean outlines and tonal illustrations from original pencil drawings using image filtering techniques, and we manually label the drawing styles. In addition, our model creates different pencil styles (e.g., line sketchiness and shading style) in a user-controllable manner. Experimental results on different types of pencil drawings show that the proposed algorithm performs favorably against existing methods in terms of quality, diversity and user evaluations.
There is a rich literature on procedural (non-learning) stylization in Non-Photorealistic Rendering (NPR) @cite_12 @cite_30 . Early work focuses on interactive pen-and-ink drawing and hatching of 2D inputs @cite_32 @cite_19 and 3D models @cite_5 @cite_31 @cite_3 . Pencil drawing is similar to pen-and-ink drawing, but it has more degrees-of-freedom since individual pencil strokes may have varying tone, width, and texture. For 2D images, several procedural image stylization approaches have simulated pencil drawings @cite_44 @cite_49 . These methods use hand-crafted algorithms and features for outlines and a pre-defined set of pencil texture examples for shading. While procedural approaches can be fast and interpretable, accurately capturing a wide range of illustration styles with purely procedural methods is still challenging.
@cite_30 @cite_32 @cite_3 @cite_44 @cite_19 @cite_49 @cite_5 @cite_31 @cite_12
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This paper surveys the field of nonphotorealistic rendering (NPR), focusing on techniques for transforming 2D input (images and video) into artistically stylized renderings. We first present a taxonomy of the 2D NPR algorithms developed over the past two decades, structured according to the design characteristics and behavior of each technique. We then describe a chronology of development from the semiautomatic paint systems of the early nineties, through to the automated painterly rendering systems of the late nineties driven by image gradient analysis. Two complementary trends in the NPR literature are then addressed, with reference to our taxonomy. First, the fusion of higher level computer vision and NPR, illustrating the trends toward scene analysis to drive artistic abstraction and diversity of style. Second, the evolution of local processing approaches toward edge-aware filtering for real-time stylization of images and video. The survey then concludes with a discussion of open challenges for 2D NPR identified in recent NPR symposia, including topics such as user and aesthetic evaluation. Non-photorealistic rendering is an important research topic in computer graphics, where painterly or stroke-based rendering has received intensive attention from researchers in recent years. The goal of this paper is to design a fully automatic algorithm, which is able to turn a photograph into an oil-style painting. Different from the existing approaches that use real brush stroke images as templates, our brush strokes were created in random manner according to the characteristics of the local image region. For determining the direction of a brush stroke, we also proposed a new method based on template-matching to evaluate the major orientation of edge features within a local image window. Moreover, a novel method of deciding stroke locations was proposed, which is simple yet effective. All these features together significantly reduce the undesirable systematic impression, which appears to be a common artifact of painterly rendering. This paper introduces a programmable approach to non-photorealistic line drawing from 3D models, inspired by programmable shaders in traditional rendering. We propose a new image creation model where all operations are controlled through user-defined procedures. A view map describing all relevant support lines in the drawing and their topological arrangement is first created from the 3D model; a number of style modules operate on this map, by procedurally selecting, chaining or splitting lines, before creating strokes and assigning drawing attributes. The resulting drawing system permits flexible control of all elements of drawing style: first, different style modules can be applied to different types of lines in a view; second, the topology and geometry of strokes are entirely controlled from the programmable modules; and third, stroke attributes are assigned procedurally and can be correlated at will with various scene or view properties. Finally, we propose new density control strategies where strokes can be adapted or omitted to avoid visual clutter. We illustrate the components of our system and show how style modules successfully capture stylized visual characteristics that can be applied across a wide range of models. We present an approach to example-based stylization of 3D renderings that better preserves the rich expressiveness of hand-created artwork. Unlike previous techniques, which are mainly guided by colors and normals, our approach is based on light propagation in the scene. This novel type of guidance can distinguish among context-dependent illumination effects, for which artists typically use different stylization techniques, and delivers a look closer to realistic artwork. In addition, we demonstrate that the current state of the art in guided texture synthesis produces artifacts that can significantly decrease the fidelity of the synthesized imagery, and propose an improved algorithm that alleviates them. Finally, we demonstrate our method's effectiveness on a variety of scenes and styles, in applications like interactive shading study or autocompletion.