  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.
