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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We present an interactive system for creating pen-and-ink illustrations. The system uses stroke textures —collections of strokes arranged in different patterns—to generate texture and tone. The user “paints” with a desired stroke texture to achieve a desired tone, and the computer draws all of the individual strokes. The system includes support for using scanned or rendered images for reference to provide the user with guides for outline and tone. By following these guides closely, the illustration system can be used for interactive digital halftoning, in which stroke textures are applied to convey details that would otherwise be lost in this black-and-white medium. By removing the burden of placing individual strokes from the user, the illustration system makes it possible to create fine stroke work with a purely mouse-based interface. Thus, this approach holds promise for bringing high-quality black-and-white illustration to the world of personal computing and desktop publishing. This paper presents new algorithms and techniques for rendering parametric free-form surfaces in pen and ink. In particular, we introduce the idea of “controlled-density hatching” for conveying tone, texture, and shape. The fine control over tone this method provides allows the use of traditional texture mapping techniques for specifying the tone of pen-and-ink illustrations. We also show how a planar map, a data structure central to our rendering algorithm, can be constructed from parametric surfaces, and used for clipping strokes and generating outlines. Finally, we show how curved shadows can be cast onto curved objects for this style of illustration. CR We present an interactive system for creating pen-and-ink-style line drawings from greyscale images in which the strokes of the rendered illustration follow the features of the original image. The user, via new interaction techniques for editing a direction field, specifies an orientation for each region of the image; the computer draws oriented strokes, based on a user-specified set of example strokes, that achieve the same tone as the image via a new algorithm that compares an adaptively-blurred version of the current illustration to the target tone image. By aligning the direction field with surface orientations of the objects in the image, the user can create textures that appear attached to those objects instead of merely conveying their darkness. The result is a more compelling pen-and-ink illustration than was previously possible from 2D reference imagery. CR This article presents an algorithm for learning hatching styles from line drawings. An artist draws a single hatching illustration of a 3D object. Her strokes are analyzed to extract the following per-pixel properties: hatching level (hatching, cross-hatching, or no strokes), stroke orientation, spacing, intensity, length, and thickness. A mapping is learned from input geometric, contextual, and shading features of the 3D object to these hatching properties, using classification, regression, and clustering techniques. Then, a new illustration can be generated in the artist's style, as follows. First, given a new view of a 3D object, the learned mapping is applied to synthesize target stroke properties for each pixel. A new illustration is then generated by synthesizing hatching strokes according to the target properties. Non-photorealistic rendering (NPR) is a combination of computer graphics and computer vision that produces renderings in various artistic, expressive or stylized ways such as painting and drawing. This book focuses on image and video based NPR, where the input is a 2D photograph or a video rather than a 3D model. 2D NPR techniques have application in areas as diverse as consumer and professional digital photography and visual effects for TV and film production. The book covers the full range of the state of the art of NPR with every chapter authored by internationally renowned experts in the field, covering both classical and contemporary techniques. It will enable both graduate students in computer graphics, computer vision or image processing and professional developers alike to quickly become familiar with contemporary techniques, enabling them to apply 2D NPR algorithms in their own projects.