This study proposes and verifies a next-generation 3D modeling pipeline integrating Generative AI and Photogrammetry techniques. Two types of objects were selected for experimentation: a bust combining characteristics of both objects and characters, and a stone slab exhibiting planar and symmetrical features. Firstly, videos depicting these objects from various viewpoints were generated using generative AI models. Secondly, these videos were processed through Photogrammetry software to produce high-quality 3D meshes and diffuse textures. Lastly, post-processing including high and Low-polygon mesh separation, UV unwrapping, and texture baking was performed, successfully producing Physically Based Rendering (PBR)-compatible game assets. The proposed pipeline demonstrates significant potential for practical applications across various industries, and further advances in generative AI and future research are expected to broaden this novel approach to digital asset creation.

ABSTRACT
1. 서론
    1.1 연구의 배경
    1.2 연구의 목적 및 방법
2. 이론적 배경
    2.1 AI 기반 이미지·영상 생성
    2.2 사진측량 기반 3D 스캐닝
3. 생성형 AI와 사진측량 기술을 결합한 3D 그래픽 데이터 제작
    3.1 연구 대상 및 사용 도구 설정
    3.3 AI 기반 이미지·영상 생성
    3.4 생성한 오브젝트 영상 확인 및 분석
    3.5 적합한 영상 선별
    3.6 사진측량 소프트웨어 적용
    3.7 데이터 후가공
4. 연구 결과 및 제안
    4.1 연구 결과
    4.2 파이프라인 제안
5. 결 론
    5.1 연구의 의의
    5.2 향후 연구과제
참고문헌
<저자소개>

• Ji-hoon AHN(NCSOFT Scan Studio, NCSOFT, Seongnam, Republic of Korea) | 안지훈
• Ki-Don PARK(NCSOFT Scan Studio, NCSOFT, Seongnam, Republic of Korea) | 박기돈
• Young-A YI(Division of Media & Contents, SungKongHoe University) | 이영아 Corresponding author