English

Ref-NPR: Reference-Based Non-Photorealistic Radiance Fields for Controllable Scene Stylization

Computer Vision and Pattern Recognition 2023-03-28 v2

Abstract

Current 3D scene stylization methods transfer textures and colors as styles using arbitrary style references, lacking meaningful semantic correspondences. We introduce Reference-Based Non-Photorealistic Radiance Fields (Ref-NPR) to address this limitation. This controllable method stylizes a 3D scene using radiance fields with a single stylized 2D view as a reference. We propose a ray registration process based on the stylized reference view to obtain pseudo-ray supervision in novel views. Then we exploit semantic correspondences in content images to fill occluded regions with perceptually similar styles, resulting in non-photorealistic and continuous novel view sequences. Our experimental results demonstrate that Ref-NPR outperforms existing scene and video stylization methods regarding visual quality and semantic correspondence. The code and data are publicly available on the project page at https://ref-npr.github.io.

Keywords

Cite

@article{arxiv.2212.02766,
  title  = {Ref-NPR: Reference-Based Non-Photorealistic Radiance Fields for Controllable Scene Stylization},
  author = {Yuechen Zhang and Zexin He and Jinbo Xing and Xufeng Yao and Jiaya Jia},
  journal= {arXiv preprint arXiv:2212.02766},
  year   = {2023}
}

Comments

Accepted by CVPR2023. 17 pages, 20 figures. Project page: https://ref-npr.github.io, Code: https://github.com/dvlab-research/Ref-NPR

R2 v1 2026-06-28T07:23:14.289Z