Modern artificial intelligence offers a novel and transformative approach to creating digital art across diverse styles and modalities like images, videos and 3D data, unleashing the power of creativity and revolutionizing the way that we perceive and interact with visual content. This paper reports on recent advances in stylized 3D asset creation and manipulation with the expressive power of neural networks. We establish a taxonomy for neural stylization, considering crucial design choices such as 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 presents in-depth discussions on recent neural stylization methods for 3D data, accompanied by a mini-benchmark evaluating selected neural field stylization methods. Based on the insights gained from the survey, we highlight the practical significance, open challenges, future research, and potential impacts of neural stylization, which facilitates researchers and practitioners to navigate the rapidly evolving landscape of 3D content creation using modern artificial intelligence.

翻译：现代人工智能为跨图像、视频和三维数据等多种风格与模态的数字艺术创作提供了新颖且变革性的方法，释放了创造力潜能，并彻底改变了我们感知与交互视觉内容的方式。本文综述了利用神经网络表达能力进行风格化三维资产创建与操控的最新进展。我们建立了神经风格化的分类体系，考量了场景表示、引导数据、优化策略及输出风格等关键设计选择。基于该分类体系，本综述首先回顾二维图像神经风格化的背景，随后深入探讨针对三维数据的最新神经风格化方法，并附上对选定神经场风格化方法进行评测的微型基准测试。基于综述所得洞见，我们着重阐述了神经风格化的实际意义、开放挑战、未来研究方向及潜在影响，以助力研究人员与从业者驾驭利用现代人工智能进行三维内容创作的快速发展格局。