Results 11 to 20 of about 131,036 (273)
Neural style transfer for 3D meshes
Graphical Models, 2023 Style transfer is a popular research topic in the field of computer vision. In 3D stylization, a mesh model is deformed to achieve a specific geometric style.
Hongyuan Kang +3 more
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Demystifying Neural Style Transfer [PDF]
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017 Neural Style Transfer has recently demonstrated very exciting results which catches eyes in both academia and industry. Despite the amazing results, the principle of neural style transfer, especially why the Gram matrices could represent style remains ...
Hou, Xiaodi +3 more
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Depth-aware neural style transfer [PDF]
Proceedings of the Symposium on Non-Photorealistic Animation and Rendering, 2017 Neural style transfer has recently received significant attention and demonstrated amazing results. An efficient solution proposed by Johnson et al. trains feed-forward convolutional neural networks by defining and optimizing perceptual loss functions ...
Cheng, Ming-Ming +3 more
core +4 more sources
Laplacian-Steered Neural Style Transfer [PDF]
Proceedings of the 25th ACM international conference on Multimedia, 2017 Neural Style Transfer based on Convolutional Neural Networks (CNN) aims to synthesize a new image that retains the high-level structure of a content image, rendered in the low-level texture of a style image. This is achieved by constraining the new image
Alexei +5 more
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Structure-Preserving Neural Style Transfer [PDF]
IEEE Transactions on Image Processing, 2020 State-of-the-art neural style transfer methods have demonstrated amazing results by training feed-forward convolutional neural networks or using an iterative optimization strategy. The image representation used in these methods, which contains two components: style representation and content representation, is typically based on high-level features ...
Cheng, Ming-Ming +5 more
openaire +3 more sources
Neural Style Transfer: A Review [PDF]
IEEE Transactions on Visualization and Computer Graphics, 2020 The seminal work of Gatys et al. demonstrated the power of Convolutional Neural Networks (CNNs) in creating artistic imagery by separating and recombining image content and style. This process of using CNNs to render a content image in different styles is referred to as Neural Style Transfer (NST).
Yongcheng Jing +5 more
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Cognitive Systems Research, 2022 Style Transfer has been proposed in a number of fields: fine arts, natural language processing, and fixed trajectories. We scale this concept up to control policies within a Deep Reinforcement Learning infrastructure. Each network is trained to maximize the expected reward, which typically encodes the goal of an action, and can be described as the ...
Fernández Fernández, Raúl +3 more
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Deep Correlation Multimodal Neural Style Transfer
IEEE Access, 2021 Style transfer is a well-known approach used to transfer the art style of a style image to an input content image, and the core method of the style transfer is to use the Gram matrix for representing the style features of images.
Nguyen Quang Tuyen +3 more
doaj +1 more source
Lagrangian neural style transfer for fluids [PDF]
ACM Transactions on Graphics, 2020 Artistically controlling the shape, motion and appearance of fluid simulations pose major challenges in visual effects production. In this paper, we present a neural style transfer approach from images to 3D fluids formulated in a Lagrangian viewpoint. Using particles for style transfer has unique benefits compared to grid-based techniques.
Kim, Byungsoo +3 more
openaire +3 more sources
An Edge Filter Based Approach of Neural Style Transfer to the Image Stylization
IEEE Access, 2022 Transferring artistic styles onto any image or photograph has become popular in industry and academia in recent years. The use of neural style transfer (NST) for image style transfer is getting more popular.
Shubham Bagwari +5 more
doaj +1 more source