  3D scenes photorealistic stylization aims to generate photorealistic images
from arbitrary novel views according to a given style image while ensuring
consistency when rendering from different viewpoints. Some existing stylization
methods with neural radiance fields can effectively predict stylized scenes by
combining the features of the style image with multi-view images to train 3D
scenes. However, these methods generate novel view images that contain
objectionable artifacts. Besides, they cannot achieve universal photorealistic
stylization for a 3D scene. Therefore, a styling image must retrain a 3D scene
representation network based on a neural radiation field. We propose a novel 3D
scene photorealistic style transfer framework to address these issues. It can
realize photorealistic 3D scene style transfer with a 2D style image. We first
pre-trained a 2D photorealistic style transfer network, which can meet the
photorealistic style transfer between any given content image and style image.
Then, we use voxel features to optimize a 3D scene and get the geometric
representation of the scene. Finally, we jointly optimize a hyper network to
realize the scene photorealistic style transfer of arbitrary style images. In
the transfer stage, we use a pre-trained 2D photorealistic network to constrain
the photorealistic style of different views and different style images in the
3D scene. The experimental results show that our method not only realizes the
3D photorealistic style transfer of arbitrary style images but also outperforms
the existing methods in terms of visual quality and consistency. Project
page:https://semchan.github.io/UPST_NeRF.
