This paper explores the burgeoning field of 3D content generation within the landscape of Artificial Intelligence Generated Content (AIGC) and large-scale models. It investigates innovative methods like Text-to-3D and Image-to-3D, which translate text or images into 3D objects, reshaping our understanding of virtual and real-world simulations. Despite significant advancements in text and image generation, automatic 3D content generation remains nascent. This paper emphasizes the urgency for further research in this area. By leveraging pre-trained diffusion models, which have demonstrated prowess in high-fidelity image generation, this paper aims to summary 3D content creation, addressing challenges such as data scarcity and computational resource limitations. Additionally, this paper discusses the challenges and proposes solutions for using pre-trained diffusion models in 3D content generation. By synthesizing relevant research and outlining future directions, this study contributes to advancing the field of 3D content generation amidst the proliferation of large-scale AIGC models.

翻译：本文探讨了人工智能生成内容（AIGC）与大规模模型背景下快速发展的三维内容生成领域。文章研究了诸如文本到三维（Text-to-3D）和图像到三维（Image-to-3D）等创新方法，这些方法可将文本或图像转化为三维物体，重塑了我们对虚拟与现实世界仿真的理解。尽管在文本与图像生成方面已取得显著进展，自动化的三维内容生成仍处于起步阶段。本文强调了在该领域开展进一步研究的紧迫性。通过利用在生成高保真度图像方面已展现出强大能力的预训练扩散模型，本文旨在综述三维内容创建，并应对数据稀缺与计算资源有限等挑战。此外，本文讨论了在三维内容生成中使用预训练扩散模型所面临的挑战，并提出了相应的解决方案。通过综合相关研究并展望未来方向，本研究有助于在大型AIGC模型蓬勃发展的背景下，推动三维内容生成领域的进步。