ARF-Plus: Controlling Perceptual Factors in Artistic Radiance Fields for 3D Scene Stylization

The radiance fields style transfer is an emerging field that has recently gained popularity as a means of 3D scene stylization, thanks to the outstanding performance of neural radiance fields in 3D reconstruction and view synthesis. We highlight a research gap in radiance fields style transfer, the lack of sufficient perceptual controllability, motivated by the existing concept in the 2D image style transfer. In this paper, we present ARF-Plus, a 3D neural style transfer framework offering manageable control over perceptual factors, to systematically explore the perceptual controllability in 3D scene stylization. Four distinct types of controls - color preservation control, (style pattern) scale control, spatial (selective stylization area) control, and depth enhancement control - are proposed and integrated into this framework. Results from real-world datasets, both quantitative and qualitative, show that the four types of controls in our ARF-Plus framework successfully accomplish their corresponding perceptual controls when stylizing 3D scenes. These techniques work well for individual style inputs as well as for the simultaneous application of multiple styles within a scene. This unlocks a realm of limitless possibilities, allowing customized modifications of stylization effects and flexible merging of the strengths of different styles, ultimately enabling the creation of novel and eye-catching stylistic effects on 3D scenes.

[1]  Dongdong Chen,et al.  NeRF-Art: Text-Driven Neural Radiance Fields Stylization , 2022, IEEE transactions on visualization and computer graphics.

[2]  Jiaya Jia,et al.  Ref-NPR: Reference-Based Non-Photorealistic Radiance Fields for Controllable Scene Stylization , 2022, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Yue Liu,et al.  UPST-NeRF: Universal Photorealistic Style Transfer of Neural Radiance Fields for 3D Scene , 2022, ArXiv.

[4]  B. Ommer,et al.  ArtFID: Quantitative Evaluation of Neural Style Transfer , 2022, GCPR.

[5]  Nicholas I. Kolkin,et al.  ARF: Artistic Radiance Fields , 2022, ECCV.

[6]  Yu-Kun Lai,et al.  StylizedNeRF: Consistent 3D Scene Stylization as Stylized NeRF via 2D-3D Mutual Learning , 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[7]  Yifan Jiang,et al.  Unified Implicit Neural Stylization , 2022, ECCV.

[8]  Andreas Geiger,et al.  TensoRF: Tensorial Radiance Fields , 2022, ECCV.

[9]  M. Nießner,et al.  StyleMesh: Style Transfer for Indoor 3D Scene Reconstructions , 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  Jian Wang,et al.  3D Photo Stylization: Learning to Generate Stylized Novel Views from a Single Image , 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Sanja Fidler,et al.  3DStyleNet: Creating 3D Shapes with Geometric and Texture Style Variations , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

[12]  Hung-Yu Tseng,et al.  Stylizing 3D Scene via Implicit Representation and HyperNetwork , 2021, 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).

[13]  Hung-Yu Tseng,et al.  Learning to Stylize Novel Views , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

[14]  Ren Ng,et al.  PlenOctrees for Real-time Rendering of Neural Radiance Fields , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

[15]  Zhifeng Yu,et al.  A Method for Arbitrary Instance Style Transfer , 2019, ArXiv.

[16]  Konrad Schindler,et al.  Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-Shot Cross-Dataset Transfer , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Zunlei Feng,et al.  Stroke Controllable Fast Style Transfer with Adaptive Receptive Fields , 2018, ECCV.

[18]  Thomas Brox,et al.  Artistic Style Transfer for Videos and Spherical Images , 2017, International Journal of Computer Vision.

[19]  Yu-Kun Lai,et al.  Depth-aware neural style transfer , 2017, NPAR '17.

[20]  Zunlei Feng,et al.  Neural Style Transfer: A Review , 2017, IEEE Transactions on Visualization and Computer Graphics.

[21]  Carlos D. Castillo,et al.  Son of Zorn's lemma: Targeted style transfer using instance-aware semantic segmentation , 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[22]  Leon A. Gatys,et al.  Controlling Perceptual Factors in Neural Style Transfer , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[23]  Leon A. Gatys,et al.  Image Style Transfer Using Convolutional Neural Networks , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Leon A. Gatys,et al.  Preserving Color in Neural Artistic Style Transfer , 2016, ArXiv.

[25]  J. Döllner,et al.  Controlling strokes in fast neural style transfer using content transforms , 2022, Vis. Comput..

[26]  B. Mildenhall Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelines , 2019 .