Abstract
In recent years, the demand for 3D content has grown exponentially with intelligent upgrading of interactive media, extended reality (XR), and Metaverse industries. In order to overcome the limitation of traditional manual modeling approaches, such as labor-intensive workflows and prolonged production cycles, revolutionary advances have been achieved through the convergence of novel 3D representation paradigms and artificial intelligence generative technologies. In this survey, we conduct a systematically review of the cutting-edge achievements in static 3D object and scene generation, as well as establish a comprehensive technical framework through systematic categorization. Specifically, we initiate our analysis with mainstream 3D object representations, followed by in-depth exploration of two principal technical pathways in object generation: data-driven supervised learning methods and deep generative model-based approaches. Regarding scene generation, we focus on three dominant paradigms: layout-guided compositional synthesis, 2D prior-based scene generation, and rule-driven modeling. Finally, we critically examine persistent challenges in 3D generation and propose potential research directions for future investigation. This survey aims to provide readers with a structured understanding of state-of-the-art 3D generation technologies while inspiring researchers to undertake more exploration in this domain.
Abstract (translated)
近年来，随着交互式媒体、扩展现实（XR）和元宇宙行业的智能化升级，对三维内容的需求呈指数级增长。为了克服传统手动建模方法的限制，如劳动密集型工作流程和延长的生产周期，通过新型三维表示范例与人工智能生成技术的融合，取得了革命性的进展。在本次综述中，我们系统地回顾了静态3D物体和场景生成领域的最新成就，并通过系统的分类建立了全面的技术框架。具体而言，我们的分析始于主流的3D对象表示形式，随后深入探讨两个主要的对象生成技术路径：数据驱动监督学习方法和基于深度生成模型的方法。对于场景生成，我们重点关注三个主导范式：布局引导组合合成、基于2D先验的场景生成以及规则驱动建模。最后，我们批判性地审视了3D生成中持续存在的挑战，并提出了未来研究方向的可能性。本次综述旨在为读者提供对最新3D生成技术结构化理解的同时，激励研究人员在此领域进行更多探索。
URL
https://arxiv.org/abs/2504.11734
