Research on Application Pathways and Development Trends of Generative Artificial Intelligence in 3D Modeling

With the rapid advancement of Generative Artificial Intelligence (AIGC, Artificial Intelligence Generated Content) technology, 3D modeling, as a core component of digital content creation, is undergoing a profound transformation from human-driven to intelligent generation. Traditional 3D modeling relies on specialized software and manual operations by modelers, characterized by complex workflows, inefficiency, and high skill barriers. In contrast, AIGC enables the automatic generation of 3D geometry, topological relationships, and texture mapping information through natural language prompts (Prompt), image inputs, or sketch instructions, significantly enhancing modeling efficiency and creative freedom. This paper systematically reviews the current primary pathways—Text-to-3D, Image-to-3D, and Sketch-to-3D—based on the technical principles of generative models. It conducts an in-depth analysis of the application characteristics of representative platforms such as MeshyAI, Kaedim, Tripo, and Hunyuan 3D. Through case studies, the feasibility and operational workflows of AIGC modeling in character asset generation, scene construction, and teaching practices are examined. Furthermore, the study comparatively analyzes the differences between AIGC and traditional modeling approaches in terms of efficiency, quality, and scalability, highlighting current challenges faced by AIGC, including precision control, limited editability, and copyright compliance. The research posits that AIGC is reconstructing the paradigm of 3D modeling, propelling 3D content production towards a new era of intelligent collaboration and low-barrier generation. Future advancements are expected to be driven by the deep integration of AIGC with Digital Content Creation (DCC) toolchains, the evolution of multimodal large models, and enhanced semantic control capabilities of Prompts. This study aims to provide a systematic reference and trend analysis for the integration of AIGC modeling technology within higher education, industry practices, and AI development.