Results 31 to 40 of about 3,164,616 (286)
Depth-Aware Neural Style Transfer for Videos
Computers, 2023 Temporal consistency and content preservation are the prominent challenges in artistic video style transfer. To address these challenges, we present a technique that utilizes depth data and we demonstrate this on real-world videos from the web, as well ...
Eleftherios Ioannou, Steve Maddock
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Image Style Transfer and Content-Style Disentanglement
CoRR, 2021 We propose a way of learning disentangled content-style representation of image, allowing us to extrapolate images to any style as well as interpolate between any pair of styles. By augmenting data set in a supervised setting and imposing triplet loss, we ensure the separation of information encoded by content and style representation. We also make use
Sailun Xu, Jiazhi Zhang, Jiamei Liu
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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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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
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Contextual Text Style Transfer [PDF]
Findings of the Association for Computational Linguistics: EMNLP 2020, 2020 We introduce a new task, Contextual Text Style Transfer - translating a sentence into a desired style with its surrounding context taken into account. This brings two key challenges to existing style transfer approaches: ($i$) how to preserve the semantic meaning of target sentence and its consistency with surrounding context during transfer; ($ii ...
Yu Cheng 0001 +5 more
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Separating Style and Content for Generalized Style Transfer [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 Neural style transfer has drawn broad attention in recent years. However, most existing methods aim to explicitly model the transformation between different styles, and the learned model is thus not generalizable to new styles. We here attempt to separate the representations for styles and contents, and propose a generalized style transfer network ...
Zhang, Yexun +3 more
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Exemplar-Based Portrait Style Transfer
IEEE Access, 2018 Transferring the style of an example image to a content image opens the door of artistic creation for end users. However, it is especially challenging for portrait photos since human vision system is sensitive to the slight artifacts on portraits ...
Ming Lu +5 more
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STEER: Unified Style Transfer with Expert Reinforcement [PDF]
, 2023 While text style transfer has many applications across natural language processing, the core premise of transferring from a single source style is unrealistic in a real-world setting.
Welleck, Sean +5 more
core
Neural Neighbor Style Transfer
CoRR, 2022 We propose Neural Neighbor Style Transfer (NNST), a pipeline that offers state-of-the-art quality, generalization, and competitive efficiency for artistic style transfer. Our approach is based on explicitly replacing neural features extracted from the content input (to be stylized) with those from a style exemplar, then synthesizing the final output ...
Nicholas I. Kolkin +5 more
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