Neural Abstract Style Transfer for Chinese Traditional Painting

Li, Bo; Xiong, Caiming; Wu, Tianfu; Zhou, Yu; Zhang, Lun; Chu, Rufeng

doi:10.1007/978-3-030-20890-5_14

Cited by 11 publications

(4 citation statements)

References 30 publications

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“…A feature transformation that employs an adaptive instance normalization technique, according to certain theories, treats each channel of the feature map as a separate Gaussian distribution, with each channel being used to establish the style of the content feature. Consistency in terms of both the standard deviations of attributes and the means of those traits is necessary; however, the channels of the feature map should be linked together in order to function properly [6,11,13]. e WCT feature transformation approach was developed by other researchers, and it considers the feature map as a multivariate Gaussian distribution, with the content features and the style features all treated as Gaussian distribution functions.…”

Section: Encoder-based Approachmentioning

confidence: 99%

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Art Style Transfer of Oil Painting Based on Parallel Convolutional Neural Network

Jin

2022

Security and Communication Networks

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To generate a new ornamental image, add an image’s oil painting style information to any image while preserving the image’s semantic content. With the rapid advancement of deep learning (DL), image style transfer has become one of the most active areas of computer vision research (CV). This paper proposes an oil painting style transfer technique based on parallel convolutional neural networks to address the ineffective style transfer of locally similar regions in content images and the slow processing speed of existing methods. By incorporating Gaussian sampling and a parallelization algorithm, this method effectively transfers the style of an oil painting. The algorithm can combine the content of any image with a variety of well-known oil painting styles to create high-quality works of art. The experimental results indicate that, compared to existing methods, the proposed method can effectively reduce the style loss of the generated image, make the generated image’s overall style more uniform, and produce a more pleasing visual effect.

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Section: Encoder-based Approachmentioning

confidence: 99%

“…is is how the term Impressionism came about. It is reasonable to argue that different epochs had their own specific aesthetics to distinguish them [6].…”

Section: Introductionmentioning

confidence: 99%

Art Style Transfer of Oil Painting Based on Parallel Convolutional Neural Network

Jin

2022

Security and Communication Networks

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“…Li et al [19] presented a framework that transfers input photograph into Chinese traditional painting style. The abstraction of Chinese traditional painting is trained and expressed through modified xDoG filters, which further process the result images of the generator.…”

Section: Deep Learning-based Tol Generation Approachmentioning

confidence: 99%

TOLGAN: An End-to-end framework for producing Traditional Orient Landscape

Kim,

Yang,

Min

2023

Preprint

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We present TOLGAN that generates traditional oriental landscape (TOL) image from a map that specifies the locations and shapes of the elements composing TOL. Users can create a TOL map by using a user interface or a segmentation scheme from a photograph. We design the generator of TOLGAN as a series of decoding layers where the map is applied between the layers. The generated TOL image is further enhanced through an AdaIN architecture. The discriminator of TOLGAN processes a generated image and its groundtruth TOL artwork image. TOLGAN is trained through a dataset composed of paired TOL artwork images and their TOL maps. We present a tool through which users can produce a TOL map by specifying and organizing the elements of TOL artworks. TOLGAN successfully generates a series of TOL images from the TOL map. We evaluate our approach using a quantitative way by estimating FID and ArtFID scores and a qualitative way by executing two user studies. Through these studies, we prove the excellence of our approach by comparing our results with those from several important existing works.

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“…Inspired by the convolutional neural network, Gatys et al [8] proposed neural style transfer (NST) in 2015, which uses the convolutional neural network to reproduce painting styles on natural pictures. Li et al [9] proposed a neural style transfer method for traditional Chinese paintings. Sheng et al [10] proposed a new model of Chinese painting style transfer, which combines four key features as restrictions into the CNN, including brush stroke, ink diffusion, space reservation, and yellowing.…”

Section: Introductionmentioning

confidence: 99%

Paint-CUT: A Generative Model for Chinese Landscape Painting Based on Shuffle Attentional Residual Block and Edge Enhancement

Sun,

Li,

2024

Applied Sciences

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As one of the precious cultural heritages, Chinese landscape painting has developed unique styles and techniques. Researching the intelligent generation of Chinese landscape paintings from photos can benefit the inheritance of traditional Chinese culture. To address detail loss, blurred outlines, and poor style transfer in present generated results, a model for generating Chinese landscape paintings from photos named Paint-CUT is proposed. In order to solve the problem of detail loss, the SA-ResBlock module is proposed by combining shuffle attention with the resblocks in the generator, which is used to enhance the generator’s ability to extract the main scene information and texture features. In order to solve the problem of poor style transfer, perceptual loss is introduced to constrain the model in terms of content and style. The pre-trained VGG is used to extract the content and style features to calculate the perceptual loss and, then, the loss can guide the model to generate landscape paintings with similar content to landscape photos and a similar style to target landscape paintings. In order to solve the problem of blurred outlines in generated landscape paintings, edge loss is proposed to the model. The Canny edge detection is used to generate edge maps and, then, the edge loss between edge maps of landscape photos and generated landscape paintings is calculated. The generated landscape paintings have clear outlines and details by adding edge loss. Comparison experiments and ablation experiments are performed on the proposed model. Experiments show that the proposed model can generate Chinese landscape paintings with clear outlines, rich details, and realistic style. Generated paintings not only retain the details of landscape photos, such as texture and outlines of mountains, but also have similar styles to the target paintings, such as colors and brush strokes. So, the generation quality of Chinese landscape paintings has improved.

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Neural Abstract Style Transfer for Chinese Traditional Painting

Cited by 11 publications

References 30 publications

Art Style Transfer of Oil Painting Based on Parallel Convolutional Neural Network

Art Style Transfer of Oil Painting Based on Parallel Convolutional Neural Network

TOLGAN: An End-to-end framework for producing Traditional Orient Landscape

Paint-CUT: A Generative Model for Chinese Landscape Painting Based on Shuffle Attentional Residual Block and Edge Enhancement

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