Abstract
The generation of high-quality 3D environments is crucial for industries such as gaming, virtual reality, and cinema, yet remains resource-intensive due to the reliance on manual processes. This study performs a systematic review of existing generative AI techniques for 3D scene generation, analyzing their characteristics, strengths, limitations, and potential for improvement. By examining state-of-the-art approaches, it presents key challenges such as scene authenticity and the influence of textual inputs. Special attention is given to how AI can blend different stylistic domains while maintaining coherence, the impact of training data on output quality, and the limitations of current models. In addition, this review surveys existing evaluation metrics for assessing realism and explores how industry professionals incorporate AI into their workflows. The findings of this study aim to provide a comprehensive understanding of the current landscape and serve as a foundation for future research on AI-driven 3D content generation. Key findings include that advanced generative architectures enable high-quality 3D content creation at a high computational cost, effective multi-modal integration techniques like cross-attention and latent space alignment facilitate text-to-3D tasks, and the quality and diversity of training data combined with comprehensive evaluation metrics are critical to achieving scalable, robust 3D scene generation.
Abstract (translated)
高质量的三维环境生成对于游戏、虚拟现实和电影等行业至关重要，但因其依赖于手动过程而仍然非常耗费资源。本研究对现有的用于生成3D场景的生成式AI技术进行了系统性回顾，分析了这些技术的特点、优势、局限性和改进潜力。通过考察最前沿的方法，它揭示了一些关键挑战，如场景的真实性以及文本输入的影响。特别关注的是，如何利用AI在保持一致性的同时融合不同的风格领域，训练数据对输出质量的影响以及当前模型的限制。此外，本综述还调研了现有的评估真实性的指标，并探讨了行业专业人士如何将AI整合到他们的工作流程中。这项研究的发现旨在为当前的研究提供一个全面的理解基础，并作为未来关于基于人工智能的3D内容生成研究的基础。 关键发现包括：先进的生成架构能够在高计算成本下创建高质量的3D内容；有效的多模态集成技术，如跨注意力和潜在空间对齐，有助于文本到3D的任务；以及高质量且多样化的训练数据结合全面的评估指标对于实现可扩展、稳健的3D场景生成至关重要。
URL
https://arxiv.org/abs/2506.05449
