Paper Info
Title
ARF: Artistic Radiance Fields.
Abstract
We present a method for transferring the artistic features of an arbitrary style image to a 3D scene. Previous methods that perform 3D stylization on point clouds or meshes are sensitive to geometric reconstruction errors for complex real-world scenes. Instead, we propose to stylize the more robust radiance field representation. We find that the commonly used Gram matrix-based loss tends to produce blurry results without faithful brushstrokes, and introduce a nearest neighbor-based loss that is highly effective at capturing style details while maintaining multi-view consistency. We also propose a novel deferred back-propagation method to enable optimization of memory-intensive radiance fields using style losses defined on full-resolution rendered images. Our extensive evaluation demonstrates that our method outperforms baselines by generating artistic appearance that more closely resembles the style image. Please check our project page for video results and open-source implementations: https://www.cs.cornell.edu/projects/arf/ .
Year
DOI
Venue
2022
10.1007/978-3-031-19821-2_41
European Conference on Computer Vision
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Kai Zhang100.34
Nick Kolkin200.34
Sai Bi3635.28
Fujun Luan400.68
Zexiang Xu510110.17
Eli Shechtman64340177.94
Noah Snavely74262197.04