Recent years have witnessed remarkable advances in artificial intelligence generated content(AIGC), with diverse input modalities, e.g., text, image, video, audio and 3D. The 3D is the most close visual modality to real-world 3D environment and carries enormous knowledge. The 3D content generation shows both academic and practical values while also presenting formidable technical challenges. This review aims to consolidate developments within the burgeoning domain of 3D content generation. Specifically, a new taxonomy is proposed that categorizes existing approaches into three types: 3D native generative methods, 2D prior-based 3D generative methods, and hybrid 3D generative methods. The survey covers approximately 60 papers spanning the major techniques. Besides, we discuss limitations of current 3D content generation techniques, and point out open challenges as well as promising directions for future work. Accompanied with this survey, we have established a project website where the resources on 3D content generation research are provided. The project page is available at https://github.com/hitcslj/Awesome-AIGC-3D.

翻译：近年来，人工智能生成内容（AIGC）取得了显著进展，涵盖了文本、图像、视频、音频和三维等多种输入模态。其中，三维是与现实世界三维环境最接近的视觉模态，承载着海量知识。三维内容生成具有重要的学术价值和实践意义，同时也面临严峻的技术挑战。本综述旨在梳理三维内容生成这一新兴领域的发展脉络。具体而言，我们提出了一种新的分类体系，将现有方法分为三类：原生三维生成方法、基于二维先验的三维生成方法以及混合三维生成方法。本综述涵盖约60篇涉及主要技术的论文。此外，我们讨论了当前三维内容生成技术的局限性，指出了尚存的挑战及未来有前景的研究方向。伴随本综述，我们建立了项目网站，提供三维内容生成研究的资源，项目页面访问地址为：https://github.com/hitcslj/Awesome-AIGC-3D。