U-VAP: User-specified Visual Appearance Personalization via Decoupled Self Augmentation

arXiv:2403.20231v1 Announce Type: new
Abstract: Concept personalization methods enable large text-to-image models to learn specific subjects (e.g., objects/poses/3D models) and synthesize renditions in new contexts. Given that the image references are highly biased towards visual attributes, state-of-the-art personalization models tend to overfit the whole subject and cannot disentangle visual characteristics in pixel space. In this study, we proposed a more challenging setting, namely fine-grained visual appearance personalization. Different from existing methods, we allow users to provide a sentence describing the …

3d models abstract art arxiv augmentation concept cs.cv image learn objects personalization state text text-to-image type via visual