A Survey on 3D Style Transfer

Qifeng Zhu; Min Sun; Jiang Wang

Tools for Design, Implementation and Verification of Emerging Information Technologies. 17th EAI International Conference, TridentCom 2022, Melbourne, Australia, November 23-25, 2022, Proceedings

Research Article

A Survey on 3D Style Transfer

Cite: BibTeX Plain Text

@INPROCEEDINGS{10.1007/978-3-031-33458-0_10,
    author={Qifeng Zhu and Min Sun and Jiang Wang},
    title={A Survey on 3D Style Transfer},
    proceedings={Tools for Design, Implementation and Verification of Emerging Information Technologies. 17th EAI International Conference, TridentCom 2022, Melbourne, Australia, November 23-25, 2022, Proceedings},
    proceedings_a={TRIDENTCOM},
    year={2023},
    month={6},
    keywords={3D Images Style Transfer Neural Networks},
    doi={10.1007/978-3-031-33458-0_10}
}

Qifeng Zhu
Min Sun
Jiang Wang
Year: 2023
A Survey on 3D Style Transfer
TRIDENTCOM
Springer
DOI: 10.1007/978-3-031-33458-0_10

Qifeng Zhu^,*, Min Sun, Jiang Wang

*Contact email: qifeng_zhu@szcu.edu.cn

Abstract

Image style transfer is a popular and widely studied task in computer vision, and it aims to apply the style of the source image to the target while the target remains its original content. Style transfer is widely used in creating new images in 2D, but style transfer in 3D images still has many challenges. In this paper, we summarize the major existing methods of 3D style transfer, including traditional and neural network based approaches. Moreover, we discuss the application field and the future research direction in 3D style transfer.

Keywords: 3D Images Style Transfer Neural Networks

Published: 2023-06-17
Appears in: SpringerLink

: http://dx.doi.org/10.1007/978-3-031-33458-0_10

A Survey on 3D Style Transfer

Abstract

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