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3D scenes photorealistic stylization aims to generate photorealistic imagesfrom arbitrary novel views according to a given style image while ensuringconsistency when rendering from different viewpoints. Some existing stylizationmethods with neural radiance fields can effectively predict stylized scenes bycombining the features of the style image with multi-view images to train 3Dscenes. However, these methods generate novel view images that containobjectionable artifacts. Besides, they cannot achieve universal photorealisticstylization for a 3D scene. Therefore, a styling image must retrain a 3D scenerepresentation network based on a neural radiation field. We propose a novel 3Dscene photorealistic style transfer framework to address these issues. It canrealize photorealistic 3D scene style transfer with a 2D style image. We firstpre-trained a 2D photorealistic style transfer network, which can meet thephotorealistic style transfer between any given content image and style image.Then, we use voxel features to optimize a 3D scene and get the geometricrepresentation of the scene. Finally, we jointly optimize a hyper network torealize the scene photorealistic style transfer of arbitrary style images. Inthe transfer stage, we use a pre-trained 2D photorealistic network to constrainthe photorealistic style of different views and different style images in the3D scene. The experimental results show that our method not only realizes the3D photorealistic style transfer of arbitrary style images but also outperformsthe existing methods in terms of visual quality and consistency. Projectpage:https://semchan.github.io/UPST_NeRF.

UPST-NeRF: Universal Photorealistic Style Transfer of Neural Radiance Fields for 3D Scene