AI와 전통 도구를 이용한 3D 모델링 비교 -효율성, 심미성, 및 시장 수용도를 중심으로-

초록

This study evaluates how AI-based 3D modeling compares to traditional 3D modeling in practical applications, analyzing the strengths and limitations of both approaches. The experiment involved using MidJourney, an AI-based image generation tool, and Blender, a traditional 3D modeling software, to create 3D models from identical sketches. The generated models were compared based on production time, visual quality, and user preference. The results showed that AI-based modeling tools significantly reduced model creation time and received high ratings for color rendering and texture quality. In contrast, traditional 3D modeling tools excelled in fine-detail representation, realistic depiction, and physically accurate lighting simulation. These findings suggest that while AI tools offer strong advantages in efficiency, they have limitations in precise adjustments and realistic texture application. This study restricted participants to professionals with at least three years of experience in 3D modeling, indicating that results may differ for general users or beginners. User surveys revealed that AI-generated models were preferred for color rendering and texture quality, whereas traditional modeling techniques were rated higher for texture refinement and material properties. These results indicate that AI modeling is effective for rapid prototyping and conceptual design but remains insufficient for high-quality, realistic 3D modeling, which still requires traditional tools. This study suggests that AI and traditional 3D modeling technologies complement each other, and a hybrid approach could be more widely adopted in industrial applications. As AI modeling advances, its role in professional workflows will expand, particularly in areas requiring speed and automation. Further research should examine how AI-generated models can be refined to meet the accuracy and realism demanded by industries such as gaming, animation, and architecture. Additionally, ethical considerations, including intellectual property and authorship of AI-generated models, should be explored to establish clearer guidelines.