Composition-based Detail Preservation in Pose Transformation Using Diffusion Models

Jae Hyun Cho, Min Seo Shin, So Hyun Kang, Jung Won Yoon, Tae Hyung Kim, Youn Kyu Lee

Abstract: Existing methods for pose transformation typically cause the loss of details within the image, such as facial features or accessories. In this paper, we propose a new composition-based pose transformation method that preserves details. Based on the given input image and text prompt, our proposed method automatically extracts the details specified by the text prompt and composites them with the pose-transformed image. Experimental results on real-world datasets confirm that our proposed method successfully transforms image poses while preserving details, which is not supported by existing pose transformation methods.

External IDs:dblp:conf/ictc/ChoSKYKL24