Modern artificial intelligence provides a novel way of producing digital art
in styles. The expressive power of neural networks enables the realm of visual
style transfer methods, which can be used to edit images, videos, and 3D data
to make them more artistic and diverse. This paper reports on recent advances
in neural stylization for 3D data. We provide a taxonomy for neural stylization
by considering several important design choices, including scene
representation, guidance data, optimization strategies, and output styles.
Building on such taxonomy, our survey first revisits the background of neural
stylization on 2D images, and then provides in-depth discussions on recent
neural stylization methods for 3D data, where we also provide a mini-benchmark
on artistic stylization methods. Based on the insights gained from the survey,
we then discuss open challenges, future research, and potential applications
and impacts of neural stylization.

用现代人工智能技术进行数字艺术的新方法是通过神经网络的表现力在图像、视频和 3D 数据中进行样式转换，本文探讨了神经样式化在 3D 数据方面的最新进展，包括场景表示、指导数据、优化策略和输出样式等多个重要设计选择，并通过对 2D 图像神经样式化的背景进行回顾，详细讨论了近期的 3D 数据神经样式化方法，并提供了一个艺术样式化方法的小型基准测试，最后根据调研结果讨论了神经样式化的挑战、未来研究和潜在应用和影响。

3D 神经风格化的进展：一份综述

Advances in 3D Neural Stylization: A Survey

In this paper, we propose a novel language-guided 3D arbitrary neural style
transfer method (CLIP3Dstyler). We aim at stylizing any 3D scene with an
arbitrary style from a text description, and synthesizing the novel stylized
view, which is more flexible than the image-conditioned style transfer.
Compared with the previous 2D method CLIPStyler, we are able to stylize a 3D
scene and generalize to novel scenes without re-train our model. A
straightforward solution is to combine previous image-conditioned 3D style
transfer and text-conditioned 2D style transfer \bigskip methods. However, such
a solution cannot achieve our goal due to two main challenges. First, there is
no multi-modal model matching point clouds and language at different feature
scales (\eg low-level, high-level). Second, we observe a style mixing issue
when we stylize the content with different style conditions from text prompts.
To address the first issue, we propose a 3D stylization framework to match the
point cloud features with text features in local and global views. For the
second issue, we propose an improved directional divergence loss to make
arbitrary text styles more distinguishable as a complement to our framework. We
conduct extensive experiments to show the effectiveness of our model on
text-guided 3D scene style transfer.

本文提出了一种新的基于语音指导的三维任意神经风格转移方法，通过改进方案解决了传统 2D 和 3D 方法不能解决的问题，匹配点云和语音的不同特征，并提高文本风格可辨别性，在多个场景下实现了高效的风格迁移。