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.

翻译：现代人工智能为数字艺术的风格化创作提供了全新途径。神经网络的强大表达能力推动了视觉风格迁移方法的发展，这些方法可用于编辑图像、视频及三维数据，使其更具艺术性与多样性。本文系统梳理了三维数据神经风格化的最新进展。通过考量场景表征、引导数据、优化策略和输出风格等关键设计要素，我们构建了神经风格化的分类体系。基于该分类体系，本综述首先回溯二维图像神经风格化的研究背景，继而深入探讨面向三维数据的最新神经风格化方法，并附设艺术风格化方法的小型基准测试。基于综述所得见解，我们进一步讨论了神经风格化领域现存挑战、未来研究方向及其潜在应用与影响。