LiveStyle -- An Application to Transfer Artistic Styles
- URL: http://arxiv.org/abs/2105.00865v1
- Date: Mon, 3 May 2021 13:50:48 GMT
- Title: LiveStyle -- An Application to Transfer Artistic Styles
- Authors: Amogh G. Warkhandkar and Omkar B. Bhambure
- Abstract summary: Style Transfer using Neural Networks refers to optimization techniques, where a content image and a style image are taken and blended.
This paper implements the Style Transfer using three different Neural Networks in form of an application that is accessible to the general population.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Art is a variety of human activities that include the production of visual,
auditory, or performing objects that express the creativity, creative concepts,
or technological abilities of the artist, intended primarily for their beauty
or emotional power to be appreciated. The renaissance of historic and forgotten
art has been made possible by modern developments in Artificial Intelligence.
Techniques for Computer Vision have long been related to such arts. Style
Transfer using Neural Networks refers to optimization techniques, where a
content image and a style image are taken and blended such that it feels like
the content image is reconstructed in the style image color palette. This paper
implements the Style Transfer using three different Neural Networks in form of
an application that is accessible to the general population thereby reviving
interest in lost art styles.
Related papers
- Towards Highly Realistic Artistic Style Transfer via Stable Diffusion with Step-aware and Layer-aware Prompt [12.27693060663517]
Artistic style transfer aims to transfer the learned artistic style onto an arbitrary content image, generating artistic stylized images.
We propose a novel pre-trained diffusion-based artistic style transfer method, called LSAST.
Our proposed method can generate more highly realistic artistic stylized images than the state-of-the-art artistic style transfer methods.
arXiv Detail & Related papers (2024-04-17T15:28:53Z) - CreativeSynth: Creative Blending and Synthesis of Visual Arts based on
Multimodal Diffusion [74.44273919041912]
Large-scale text-to-image generative models have made impressive strides, showcasing their ability to synthesize a vast array of high-quality images.
However, adapting these models for artistic image editing presents two significant challenges.
We build the innovative unified framework Creative Synth, which is based on a diffusion model with the ability to coordinate multimodal inputs.
arXiv Detail & Related papers (2024-01-25T10:42:09Z) - Generative AI Model for Artistic Style Transfer Using Convolutional
Neural Networks [0.0]
Artistic style transfer involves fusing the content of one image with the artistic style of another to create unique visual compositions.
This paper presents a comprehensive overview of a novel technique for style transfer using Convolutional Neural Networks (CNNs)
arXiv Detail & Related papers (2023-10-27T16:21:17Z) - Impressions: Understanding Visual Semiotics and Aesthetic Impact [66.40617566253404]
We present Impressions, a novel dataset through which to investigate the semiotics of images.
We show that existing multimodal image captioning and conditional generation models struggle to simulate plausible human responses to images.
This dataset significantly improves their ability to model impressions and aesthetic evaluations of images through fine-tuning and few-shot adaptation.
arXiv Detail & Related papers (2023-10-27T04:30:18Z) - Learning to Evaluate the Artness of AI-generated Images [64.48229009396186]
ArtScore is a metric designed to evaluate the degree to which an image resembles authentic artworks by artists.
We employ pre-trained models for photo and artwork generation, resulting in a series of mixed models.
This dataset is then employed to train a neural network that learns to estimate quantized artness levels of arbitrary images.
arXiv Detail & Related papers (2023-05-08T17:58:27Z) - Not Only Generative Art: Stable Diffusion for Content-Style
Disentanglement in Art Analysis [23.388338598125195]
GOYA is a method that distills the artistic knowledge captured in a recent generative model to disentangle content and style.
Experiments show that synthetically generated images sufficiently serve as a proxy of the real distribution of artworks.
arXiv Detail & Related papers (2023-04-20T13:00:46Z) - 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) - Art Creation with Multi-Conditional StyleGANs [81.72047414190482]
A human artist needs a combination of unique skills, understanding, and genuine intention to create artworks that evoke deep feelings and emotions.
We introduce a multi-conditional Generative Adversarial Network (GAN) approach trained on large amounts of human paintings to synthesize realistic-looking paintings that emulate human art.
arXiv Detail & Related papers (2022-02-23T20:45:41Z) - Art Style Classification with Self-Trained Ensemble of AutoEncoding
Transformations [5.835728107167379]
Artistic style of a painting is a rich descriptor that reveals both visual and deep intrinsic knowledge about how an artist uniquely portrays and expresses their creative vision.
In this paper, we investigate the use of deep self-supervised learning methods to solve the problem of recognizing complex artistic styles with high intra-class and low inter-class variation.
arXiv Detail & Related papers (2020-12-06T21:05:23Z) - Neural Style Transfer for Remote Sensing [0.0]
The purpose of this study is to present a method for creating artistic maps from satellite images, based on the NST algorithm.
This method includes three basic steps (i.e. application of semantic image segmentation on the original satellite image, dividing its content into classes, application of neural style transfer for each class and creation of a collage)
arXiv Detail & Related papers (2020-07-31T09:30:48Z) - State of the Art on Neural Rendering [141.22760314536438]
We focus on approaches that combine classic computer graphics techniques with deep generative models to obtain controllable and photo-realistic outputs.
This report is focused on the many important use cases for the described algorithms such as novel view synthesis, semantic photo manipulation, facial and body reenactment, relighting, free-viewpoint video, and the creation of photo-realistic avatars for virtual and augmented reality telepresence.
arXiv Detail & Related papers (2020-04-08T04:36:31Z)
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.