원문정보
초록
영어
With the rapid development of generative artificial intelligence technologies, traditional workflows in 2D image creation and 3D modeling are being restructured through intelligent approaches. This paper proposes an automated modeling pipeline based on the DeepSeek text generation system and AI-based image/model transformation tools. The pipeline is designed to initiate from textual descriptions, generate corresponding 2D images through AI illustration, and then automatically convert them into 3D models with texture mapping. Empirical evaluations demonstrate that this workflow not only significantly enhances modeling efficiency but also lowers the entry barrier to 3D modeling, offering a novel path for content generation in virtual reality, digital art, and the gaming industry.
목차
1. Introduction
2. Related Work
2.1 Text-to-Image Generation
2.2 Image-to-3D Reconstruction
2.3 AI-based Texture and UV Mapping
3. Methodology
3.1 System Structure Overview
3.2 Keyword and Image Description Generation
3.3 Image Generation
3.4 Image-to-3D Modeling
3.5 Texture Adjustment and Rendering
4. Experiments and Case Study
4.1 Experimental Process Overview
4.2 Case Demonstrations and Analysis
4.3 Comparative Evaluation: AI-generated vs Manually Modeled Assets
4.4 Analysis of limitations
5. Discussion
5.1 Broader Application Scenarios
5.2 Limitations of Texture Mapping and Directions for Improvement
5.3 Limitations in User Control and Interface Design Recommendations
6. Conclusion
References