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2021
Neural style transfer (NST) is a computer vision technique that takes two images – a content image and a style reference image – and blends them together so that the resulting output image retains the core elements of the content image but appears to be painted in the style of the style reference image.
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Neural style transfer (NST) is a computer vision technique that takes two images – a content image and a style reference image – and blends them together so that the resulting output image retains the core elements of the content image but appears to be painted in the style of the style reference image.
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Music Style Transfer with Time-Varying Inversion of Diffusion Models
AAAI Conference on Artificial IntelligenceWith the development of diffusion models, text-guided image style transfer has demonstrated great controllable and high-quality results. However, the utilization of text for diverse music style transfer poses significant challenges, primarily due to the ...
Sifei Li +5 more
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2017
ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Apr 2018, Calgary, France ...
Grinstein, Eric +3 more
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ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Apr 2018, Calgary, France ...
Grinstein, Eric +3 more
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SGSST: Scaling Gaussian Splatting Style Transfer
Computer Vision and Pattern RecognitionApplying style transfer to a full 3D environment is a challenging task that has seen many developments since the advent of neural rendering. 3D Gaussian splatting (3DGS) has recently pushed further many limits of neural rendering in terms of training ...
B. Galerne +3 more
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StyleStudio: Text-Driven Style Transfer with Selective Control of Style Elements
Computer Vision and Pattern RecognitionText-driven style transfer aims to merge the style of a reference image with content described by a text prompt. Recent advancements in text-to-image models have improved the nuance of style transformations, yet significant challenges remain ...
Mingkun Lei +4 more
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StyleSplat: 3D Object Style Transfer with Gaussian Splatting
arXiv.orgRecent advancements in radiance fields have opened new avenues for creating high-quality 3D assets and scenes. Style transfer can enhance these 3D assets with diverse artistic styles, transforming creative expression.
Sahil Jain +3 more
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Multi-Source Style Transfer via Style Disentanglement Network
IEEE transactions on multimediaDespite the great success of deep neural networks for style transfer tasks, the entanglement of content and style in images leads to more style information not being captured.
Quan Wang +4 more
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Artistic Neural Style Transfer Algorithms with Activation Smoothing
Proceedings of the 2025 2nd International Conference on Informatics Education and Computer Technology ApplicationsThe works of Gatys et al. [1], [2], demonstrated the capability of Convolutional Neural Networks in creating artistic style images. This process of transferring content images in different styles is called Neural Style Transfer (NST).
Xiangtian Li +5 more
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ST-ITO: Controlling Audio Effects for Style Transfer with Inference-Time Optimization
International Society for Music Information Retrieval ConferenceAudio production style transfer is the task of processing an input to impart stylistic elements from a reference recording. Existing approaches often train a neural network to estimate control parameters for a set of audio effects.
C. Steinmetz +6 more
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TinyStyler: Efficient Few-Shot Text Style Transfer with Authorship Embeddings
Conference on Empirical Methods in Natural Language ProcessingThe goal of text style transfer is to transform the style of texts while preserving their original meaning, often with only a few examples of the target style.
Zachary Horvitz +5 more
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