Chinese Painting Style Transfer Using Deep Generative Models
- URL: http://arxiv.org/abs/2310.09978v2
- Date: Tue, 17 Oct 2023 18:15:15 GMT
- Title: Chinese Painting Style Transfer Using Deep Generative Models
- Authors: Weijian Ma, Yanyang Kong
- Abstract summary: Artistic style transfer aims to modify the style of the image while preserving its content.
We will study and leverage different state-of-the-art deep generative models for Chinese painting style transfer.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Artistic style transfer aims to modify the style of the image while
preserving its content. Style transfer using deep learning models has been
widely studied since 2015, and most of the applications are focused on specific
artists like Van Gogh, Monet, Cezanne. There are few researches and
applications on traditional Chinese painting style transfer. In this paper, we
will study and leverage different state-of-the-art deep generative models for
Chinese painting style transfer and evaluate the performance both qualitatively
and quantitatively. In addition, we propose our own algorithm that combines
several style transfer models for our task. Specifically, we will transfer two
main types of traditional Chinese painting style, known as "Gong-bi" and
"Shui-mo" (to modern images like nature objects, portraits and landscapes.
Related papers
- DLP-GAN: learning to draw modern Chinese landscape photos with
generative adversarial network [20.74857981451259]
Chinese landscape painting has a unique and artistic style, and its drawing technique is highly abstract in both the use of color and the realistic representation of objects.
Previous methods focus on transferring from modern photos to ancient ink paintings, but little attention has been paid to translating landscape paintings into modern photos.
arXiv Detail & Related papers (2024-03-06T04:46:03Z) - ArtBank: Artistic Style Transfer with Pre-trained Diffusion Model and
Implicit Style Prompt Bank [9.99530386586636]
Artistic style transfer aims to repaint the content image with the learned artistic style.
Existing artistic style transfer methods can be divided into two categories: small model-based approaches and pre-trained large-scale model-based approaches.
We propose ArtBank, a novel artistic style transfer framework, to generate highly realistic stylized images.
arXiv Detail & Related papers (2023-12-11T05:53:40Z) - CCLAP: Controllable Chinese Landscape Painting Generation via Latent
Diffusion Model [54.74470985388726]
controllable Chinese landscape painting generation method named CCLAP.
Our method achieves state-of-the-art performance, especially in artfully-composed and artistic conception.
arXiv Detail & Related papers (2023-04-09T04:16:28Z) - QuantArt: Quantizing Image Style Transfer Towards High Visual Fidelity [94.5479418998225]
We propose a new style transfer framework called QuantArt for high visual-fidelity stylization.
Our framework achieves significantly higher visual fidelity compared with the existing style transfer methods.
arXiv Detail & Related papers (2022-12-20T17:09:53Z) - Inversion-Based Style Transfer with Diffusion Models [78.93863016223858]
Previous arbitrary example-guided artistic image generation methods often fail to control shape changes or convey elements.
We propose an inversion-based style transfer method (InST), which can efficiently and accurately learn the key information of an image.
arXiv Detail & Related papers (2022-11-23T18:44:25Z) - Domain Enhanced Arbitrary Image Style Transfer via Contrastive Learning [84.8813842101747]
Contrastive Arbitrary Style Transfer (CAST) is a new style representation learning and style transfer method via contrastive learning.
Our framework consists of three key components, i.e., a multi-layer style projector for style code encoding, a domain enhancement module for effective learning of style distribution, and a generative network for image style transfer.
arXiv Detail & Related papers (2022-05-19T13:11:24Z) - Pastiche Master: Exemplar-Based High-Resolution Portrait Style Transfer [103.54337984566877]
Recent studies on StyleGAN show high performance on artistic portrait generation by transfer learning with limited data.
We introduce a novel DualStyleGAN with flexible control of dual styles of the original face domain and the extended artistic portrait domain.
Experiments demonstrate the superiority of DualStyleGAN over state-of-the-art methods in high-quality portrait style transfer and flexible style control.
arXiv Detail & Related papers (2022-03-24T17:57:11Z) - Image Style Transfer: from Artistic to Photorealistic [0.8528384027684192]
The rapid advancement of deep learning has significantly boomed the development of photorealistic style transfer.
In this review, we reviewed the development of photorealistic style transfer starting from artistic style transfer and the contribution of traditional image processing techniques on photorealistic style transfer.
arXiv Detail & Related papers (2022-03-12T03:22:05Z) - Drafting and Revision: Laplacian Pyramid Network for Fast High-Quality
Artistic Style Transfer [115.13853805292679]
Artistic style transfer aims at migrating the style from an example image to a content image.
Inspired by the common painting process of drawing a draft and revising the details, we introduce a novel feed-forward method named Laplacian Pyramid Network (LapStyle)
Our method can synthesize high quality stylized images in real time, where holistic style patterns are properly transferred.
arXiv Detail & Related papers (2021-04-12T11:53:53Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.