生成式 AI 遇见 3D：AIGC 时代文本生成 3D 综述
Generative AI meets 3D: A Survey on Text-to-3D in AIGC Era
AI总结：
本文综述 AIGC 时代的文本生成 3D 研究，系统梳理 3D 表示、核心技术、基线、研究方向、应用场景及未来发展议程。
摘要：
近年来，生成式 AI 取得了显著进展，其中文本引导的内容生成最具实用性，因为它促进了人类指令与 AI 生成内容（AIGC）之间的交互。得益于文本生成图像以及神经辐射场（NeRF）等 3D 建模技术的发展，文本生成 3D 已成为一个新兴但极为活跃的研究领域。我们针对这一主题开展了全面综述，并跟踪整个领域随后的研究进展，旨在帮助对该方向感兴趣的读者快速跟上其快速发展。首先，我们介绍 3D 数据表示，包括结构化数据和非结构化数据。在这一预备知识的基础上，我们介绍用于获得令人满意的文本生成 3D 结果的各类核心技术。此外，我们展示近期文本生成 3D 技术中的主流基线和研究方向，包括保真度、效率、一致性、可控性、多样性和适用性。进一步地，我们总结文本生成 3D 技术在多种应用中的使用，包括化身生成、纹理生成、场景生成和 3D 编辑。最后，我们讨论文本生成 3D 未来发展的议程。
英文摘要：
Generative AI has made significant progress in recent years, with text-guided content generation being the most practical as it facilitates interaction between human instructions and AI-generated content (AIGC). Thanks to advancements in text-to-image and 3D modeling technologies, like neural radiance field (NeRF), text-to-3D has emerged as a nascent yet highly active research field. Our work conducts a comprehensive survey on this topic and follows up on subsequent research progress in the overall field, aiming to help readers interested in this direction quickly catch up with its rapid development. First, we introduce 3D data representations, including both Structured and non-Structured data. Building on this pre-requisite, we introduce various core technologies to achieve satisfactory text-to-3D results. Additionally, we present mainstream baselines and research directions in recent text-to-3D technology, including fidelity, efficiency, consistency, controllability, diversity, and applicability. Furthermore, we summarize the usage of text-to-3D technology in various applications, including avatar generation, texture generation, scene generation and 3D editing. Finally, we discuss the agenda for the future development of text-to-3D.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
