Multi‐Attention Fusion Artistic Radiance Fields and Beyond

ABSTRACT We present MRF (multi‐attention fusion artistic radiance fields), a novel approach to 3D scene stylisation that synthesises artistic rendering by integrating stylised 2D images with neural radiance fields. Our method effectively incorporates high‐frequency stylistic elements from 2D artistic representations while maintaining geometric consistency across multiple viewpoints. To address the challenges of view‐dependent stylisation coherence and semantic fidelity, we introduce two key components: (1) a multi‐scale attention module (MAM) that facilitates hierarchical feature extraction and fusion across different spatial resolutions and (2) a CLIP‐guided semantic consistency module that preserves the underlying scene structure during style transfer. Through extensive experimentation, we demonstrate that MRF achieves superior stylisation quality and detail preservation compared to state‐of‐the‐art methods, particularly in capturing fine artistic details while maintaining view consistency. Our approach represents a significant advancement in neural rendering‐based artistic stylisation of 3D scenes.

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Publication Details

Journal
IET Computer Vision
Published
2025-01-01
DOI
https://doi.org/10.1049/cvi2.70017
Primary Topic
Computer Graphics and Visualization Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

Multi‐Attention Fusion Artistic Radiance Fields and Beyond

Kunze Jiang, Jincheng Li, Yufan Zhou, Qianru Chen et al.
IET Computer Vision
Computer Graphics and Visualization Techniques
article

Multi‐Attention Fusion Artistic Radiance Fields and Beyond

Kunze Jiang, Jincheng Li, Yufan Zhou, Qianru Chen, Chao Wu, Xintong Hou
article en

Abstract

ABSTRACT We present MRF (multi‐attention fusion artistic radiance fields), a novel approach to 3D scene stylisation that synthesises artistic rendering by integrating stylised 2D images with neural radiance fields. Our method effectively incorporates high‐frequency stylistic elements from 2D artistic representations while maintaining geometric consistency across multiple viewpoints. To address the challenges of view‐dependent stylisation coherence and semantic fidelity, we introduce two key components: (1) a multi‐scale attention module (MAM) that facilitates hierarchical feature extraction and fusion across different spatial resolutions and (2) a CLIP‐guided semantic consistency module that preserves the underlying scene structure during style transfer. Through extensive experimentation, we demonstrate that MRF achieves superior stylisation quality and detail preservation compared to state‐of‐the‐art methods, particularly in capturing fine artistic details while maintaining view consistency. Our approach represents a significant advancement in neural rendering‐based artistic stylisation of 3D scenes.

IET Computer VisionVol. 19(1)
University of Technology Sydney (AU), Universiti Sains Malaysia (MY), Jiangxi Normal University (CN), Zhejiang University (CN)
Zhejiang University
Sustainable cities and communities
Openalex Percentile: Top 75%
Computer Graphics and Visualization Techniques
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Multi‐Attention Fusion Artistic Radiance Fields and Beyond — Kunze Jiang, Jincheng Li, et al. · IET Computer Vision (2025) | TGRS Research Map | TGRS