인공지능을 사용한 가상 의상 착용 네트워크는 사용자의 신체 이미지에 원하는 의상을 가상으로 착용하는 기술이다. 최근 제안된 모델들은 높은 성능을 보여줌에도 불구하고 학습 데이터에 의존성을 가지는 일반화 문제에 직면한다. 네트워크에서 사용되는 학습 데이터는 사람 전신 이미지와 착용할 의상이미지가 쌍으로 주어진다. 하지만 정자세의 전신 이미지와 배경이 제거된 의상 이미지는 학습과 검증 과정에서 높은 성능을 보여줄 수 있지만, 일반적으로 얻을 수 있는 이미지는 자세가 다양하고 배경이 포함되어있어 성능 저하의 원인이 된다. 본 논문은 Diffusion model에서 사용되는 Inpainting 기술에 Clothing Bbox Module과 Clothes Geometric Transformation을 적용하여, 다양한 자세와 배경이 포함된 의상에 높은 일반화 성능을 보여줄 수 있는 DiSAIn-VTON 모델을 제안한다. 본 논문에서는 새로 수집된 데이터 세트를 사용하여 실험하였고, 실험을 통해 모델의 우수성을 정략적, 정성적으로 증명한다.
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