미드저니를 활용한 모빌리티 분야 CMF 텍스처 이미지 생성 연구 - 프롬프트 엔지니어링과 실무 적합성 검증 -

초록

Background In the field of mobility design, CMF (Color, Material, Finish) has emerged as a key factor in defining brand identity and emotional value. Generative AI tools such as MidJourney enable the rapid creation of CMF textures with simple prompt inputs. However, the lack of systematic prompt engineering guidelines has led to ongoing issues related to quality and consistency. Methods This study categorizes CMF elements into three components: color, material, and finish. It then establishes optimized prompt keywords and structures for each component. Using MidJourney (v5.2), AI-generated textures applicable to mobility design were created and evaluated for quality. Additionally, practical verification was conducted using 3D rendering software, along with expert feedback, to analyze the applicability of AI-generated CMF textures in real-world scenarios. Result Prompts focused on material properties yielded the most accurate results, whereas color- and finish-oriented prompts often generated inaccurate images. Furthermore, issues such as low resolution and tiling (tileability) necessitated post-processing steps, including upscaling and seam removal using Photoshop. Conclusion AI-generated CMF textures have proven to be a fast and efficient tool for design exploration. However, limitations remain, including discrepancies from real materials and inconsistencies in generated outputs. Further research is needed to bridge the gap between AI-generated textures and physical CMF materials. Future studies should explore comparisons between different AI models and the integration of AR/VR technologies to enhance the realism and practical application of AI-generated CMF designs.

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