Image neural style transfer: A review

Cai, Qiang; Ma, Mengxu; Wang, Chen; Li, Haisheng

doi:10.1016/j.compeleceng.2023.108723

Cited by 17 publications

(4 citation statements)

References 13 publications

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“…GAN-based image-to-image translation: GANs 12 enable tasks such as image translation, 2,13,14 generation [15][16][17] and style transfer. 18 Image translation maps source X → target Y domains. CycleGAN 2 introduced unsupervised arbitrary domain translation via cyclic mappings.…”

Section: Related Workmentioning

confidence: 99%

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Multi‐style cartoonization: Leveraging multiple datasets with generative adversarial networks

Cai,

Li,

Nan

et al. 2024

Computer Animation & Virtual

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Scene cartoonization aims to convert photos into stylized cartoons. While generative adversarial networks (GANs) can generate high‐quality images, previous methods focus on individual images or single styles, ignoring relationships between datasets. We propose a novel multi‐style scene cartoonization GAN that leverages multiple cartoon datasets jointly. Our main technical contribution is a multi‐branch style encoder that disentangles representations to model styles as distributions over entire datasets rather than images. Combined with a multi‐task discriminator and perceptual losses optimizing across collections, our model achieves state‐of‐the‐art diverse stylization while preserving semantics. Experiments demonstrate that by learning from inter‐dataset relationships, our method translates photos into cartoon images with improved realism and abstraction fidelity compared to prior arts, without iterative re‐training for new styles.

show abstract

Section: Related Workmentioning

confidence: 99%

“…GAN‐based image‐to‐image translation : GANs 12 enable tasks such as image translation, 2,13,14 generation 15–17 and style transfer 18 . Image translation maps source

X\to

target

Y

domains.…”

Section: Related Workmentioning

confidence: 99%

Multi‐style cartoonization: Leveraging multiple datasets with generative adversarial networks

Cai,

Li,

Nan

et al. 2024

Computer Animation & Virtual

View full text Add to dashboard Cite

show abstract

“…Our goal is transforming a source photo x p ∈ X P into a target cartoon c c ∈ C c without strict pairings between datasets. We address this problem through a model GAN-based image-to-image translation: GANs 12 enable tasks such as image translation 2,13,14 , generation 15,16,17 and style transfer 18 . Image translation maps source X → target Y domains.…”

Section: Related Workmentioning

confidence: 99%

Multi-Style Cartoonization: Leveraging Multiple Datasets with GANs

Cai,

Li,

Nan

et al. 2024

Preprint

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Scene cartoonization aims to convert photos into stylized cartoons. While GANs can generate high-quality images, previous methods focus on individual images or single styles, ignoring relationships between datasets. We propose a novel multi-style scene cartoonization GAN that leverages multiple cartoon datasets jointly. Our main technical contribution is a multi-branch style encoder that disentangles representations to model styles as distributions over entire datasets rather than images. Combined with a multi-task discriminator and perceptual losses optimizing across collections, our model achieves state-of-the-art diverse stylization while preserving semantics. Experiments demonstrate that by learning from inter-dataset relationships, our method translates photos into cartoon images with improved realism and abstraction fidelity compared to prior arts, without iterative re-training for new styles.

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“…In addition to Creative Computing, AI can also be supported to produce Art with the Computer. NST applications can be used with or without CNN and GAN in the background [22,24]. These systems involve working on a group of images to produce similar images and transferring the texture and styles of images onto other images.…”

Section: Related Workmentioning

confidence: 99%

Generating Personalized Abstract Art Paintings by using People's Life Energy Distribution

Ayas,

Dalkılıç

2023

IJMSIT

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Data visualization, which is essentially the visual expression of data through mathematical calculations, is a highly effective method for transferring and understanding data. The right data visualization, selected or developed with data and people in mind, has an important role in improving the quality of conveying the relationships, meanings, information, structures and hidden properties of data to individuals from different fields of expertise and enables them to work together.Data visualization has been a subject that has evolved and progressed in different ways throughout history and has finally reached today's computer technology and conditions. The improved facilities of today's computer such as high computing power has brought data visualization methods to a new horizon. Also the changing definition of the data has expanded the boundaries of the data visualization. Today, data is obtained from many different sources such as; IoT, Embedded systems, Social data, Business data and Real-Life data. This study is also inspired by the Real-Life, and a visualization system was developed based on real-life botanical trees and using humans as data.Throughout history, computer science and other branches of science have used art as a subject in various studies. This study reunites science and art and proposes a way of expressing the human through art. Thus, an artistic visualization system was developed that uses the activities of people's lives as data and generates artistic visuals inspired by abstract art paintings. Through this study, a sub-art style was created with a visualization system that manages to produce artistic visuals by adhering to mathematical foundations.

show abstract

Image neural style transfer: A review

Cited by 17 publications

References 13 publications

Multi‐style cartoonization: Leveraging multiple datasets with generative adversarial networks

Multi‐style cartoonization: Leveraging multiple datasets with generative adversarial networks

Multi-Style Cartoonization: Leveraging Multiple Datasets with GANs

Generating Personalized Abstract Art Paintings by using People's Life Energy Distribution

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