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，涵盖文本、图像、视频、音频及3D等多种输入模态。其中，3D作为最接近真实三维环境的视觉模态，承载着丰富的知识信息。3D内容生成兼具学术价值与实践意义，同时也面临着严峻的技术挑战。本综述旨在整合3D内容生成这一新兴领域的最新进展，提出了一种新的分类体系，将现有方法划分为三类：原生3D生成方法、基于2D先验的3D生成方法，以及混合3D生成方法。综述涵盖了约60篇涉及主要技术的研究论文，并探讨了当前3D内容生成技术的局限性，指出了待解决的开放挑战及未来有前景的研究方向。配合本综述，我们搭建了项目网站，提供3D内容生成研究的相关资源。项目页面访问地址：https://github.com/hitcslj/Awesome-AIGC-3D。