Multi‐Attention Fusion Artistic Radiance Fields and Beyond
Qianru Chen, Yufan Zhou, Xintong Hou, Kunze Jiang, Jincheng Li, Chao Wu · IET Computer Vision · 2025
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.