PencilArt: A Chromatic Penciling Style Generation Framework

Gao, Chengying; Tang, Meng-Yue; Liang, Xiangguo; Su, Zhuo; Zhang, Changqing

doi:10.1111/cgf.13334

Cited by 5 publications

(7 citation statements)

References 29 publications

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“…We performed our method on a desktop computer with a 3.2‐GHz Intel Core i7 processor and 12‐GB memory. As in the work of Gao et al, we also use the experiment results of Lu et al to train our stroke generation network. The training details can be seen in the paper of Gao et al…”

Section: Resultsmentioning

confidence: 99%

“…Chen et al proposed to use a fully convolutional network to accelerate a wide variety of image processing operators, including pencil drawing. Gao et al took advantage of the deep neural network to produce the structural outline with proper intensity variation and followed the painting process of artists to adjust the tone of input images to match the luminance histogram and pencil textures of real drawings.…”

Section: Related Workmentioning

confidence: 99%

“…Artists usually determine the stroke weight and expression of an environment luminance by the geometry information of a scene. According to Gao et al, we can obtain the geometry information by perceptual cues analysis. Since we have extracted stroke images of a spatial image pyramid for imitating artists' drawing procedure, we can use the information from the former pyramid layer as the current layer's prior attention information.…”

Section: Approachmentioning

confidence: 99%

See 2 more Smart Citations

SRNPD: Spatial rendering network for pencil drawing stylization

Jin

Sheng

et al. 2019

Computer Animation & Virtual

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Pencil drawing is a simple yet effective way to depict what people see by clearly presenting details of the scene. Existing methods usually extract strokes of the input image and adjust the result image tone to make it look like a pencil drawing. However, they do not consider the quality of the stroke image and the geometry information of lines in the stroke image, which unavoidably results in the violation of original essential structures and in a flatten pencil drawing with unrealistic appearance. We put forward a spatial rendering network for pencil drawing stylization. Spatial stroke images are extracted from the image pyramid by a single‐shot bottom‐up neural network to improve the quality of these stroke images. Unlike the former tone adjustment–based methods, we analyze perceptual cues of strokes at different stroke image levels and use the obtained geometry information to constrain the stroke shading procedure. The final pencil drawing result is achieved by the stroke shading fusion of different levels' shading results. The effectiveness of our spatial rendering network for pencil drawing stylization is demonstrated by an ablation study, comparison to the state of the art, and a user study.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Approachmentioning

confidence: 99%

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SRNPD: Spatial rendering network for pencil drawing stylization

Jin

Sheng

et al. 2019

Computer Animation & Virtual

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show abstract

“…However, it was argued that this was due to the downsizing of the input images that was performed in order to improve computational efficiency. Gao et al (2018) tackled pencil drawing using a vectorized CNN model (Ren and Xu, 2015) and given the difficulty in obtaining suitable ground truth they used a modified version of Lu et al (2012) to generate the training set. In order to improve the quality of the training data, they manually adjusted the parameters of Lu et al's algorithm to obtain the best result.…”

Section: D Image-based Line Detectionmentioning

confidence: 99%

Sketch-based interaction and modeling: where do we stand?

Bonnici

Akman

Calleja

et al. 2019

AIEDAM

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Sketching is a natural and intuitive communication tool used for expressing concepts or ideas which are difficult to communicate through text or speech alone. Sketching is therefore used for a variety of purposes, from the expression of ideas on two-dimensional (2D) physical media, to object creation, manipulation, or deformation in three-dimensional (3D) immersive environments. This variety in sketching activities brings about a range of technologies which, while having similar scope, namely that of recording and interpreting the sketch gesture to effect some interaction, adopt different interpretation approaches according to the environment in which the sketch is drawn. In fields such as product design, sketches are drawn at various stages of the design process, and therefore, designers would benefit from sketch interpretation technologies which support these differing interactions. However, research typically focuses on one aspect of sketch interpretation and modeling such that literature on available technologies is fragmented and dispersed. In this paper, we bring together the relevant literature describing technologies which can support the product design industry, namely technologies which support the interpretation of sketches drawn on 2D media, sketch-based search interactions, as well as sketch gestures drawn in 3D media. This paper, therefore, gives a holistic view of the algorithmic support that can be provided in the design process. In so doing, we highlight the research gaps and future research directions required to provide full sketch-based interaction support.

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“…The results of the tests offered guidelines for generating oilfilm-like images from various color photo images. In [31], Gao et al proposed a framework for chromatic penciling style generation from wild photographs. According to a series of comparative experiments with previous work, the proposed method more accurately captured the main features of real chromatic pencil drawings and the results had an improved visual appearance.…”

Section: Related Workmentioning

confidence: 99%

Parallel Fast Pencil Drawing Generation Algorithm Based on GPU

Qiu

Liu

et al. 2019

IEEE Access

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With the development of image processing technology, pencil drawing has been widely used in video games and mobile phone applications. However, the existing pencil drawing algorithms require a large amount of time to convert a real picture into a pencil drawing; hence, it is difficult to apply them to real-time systems. This paper proposes a parallel fast pencil drawing generation algorithm based on the graphics processing unit (GPU) to accelerate the real-time rendering process of sketch painting. The parallelism of the pencil drawing generation algorithm is identified via a theoretical analysis at first. Then, sub-algorithms of the sequential algorithm are designed in parallel using the compute unified device architecture (CUDA) programming model and executed via thread-level parallel techniques. Furthermore, an optimal cache pattern of data that reduce the access time of the most frequently used data is structured using shared memory and constant memory. Finally, task-level parallelism is achieved by the CUDA stream technology, which overlaps independent sub-tasks for further acceleration. On the CUDA platform, the experimental results demonstrate that the proposed parallel algorithm can achieve a significant increase in speedup. The proposed algorithm achieves a performance improvement of 448.59 times compared with the sequential algorithm, on 2560×1920-resolution images, and maintains a high degree of similarity with the real pencil paintings. Hence, the proposed algorithm is suitable for real-time pencil drawing rendering and has promising application prospects in non-photorealistic rendering. INDEX TERMS Non-photorealistic rendering, pencil drawing, parallel algorithm, GPU platform, convolution operation, CUDA.

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PencilArt: A Chromatic Penciling Style Generation Framework

Cited by 5 publications

References 29 publications

SRNPD: Spatial rendering network for pencil drawing stylization

SRNPD: Spatial rendering network for pencil drawing stylization

Sketch-based interaction and modeling: where do we stand?

Parallel Fast Pencil Drawing Generation Algorithm Based on GPU

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