학술논문

FaceChain-SuDe: Building Derived Class to Inherit Category Attributes for One-Shot Subject-Driven Generation
Document Type
Conference
Source
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) CVPR Computer Vision and Pattern Recognition (CVPR), 2024 IEEE/CVF Conference on. :7215-7224 Jun, 2024
Subject
Computing and Processing
Computer vision
Codes
Object oriented modeling
Semantics
Buildings
Text to image
Pattern recognition
subject-driven generation
derived class
Language
ISSN
2575-7075
Abstract
Recently, subject-driven generation has garnered significant interest due to its ability to personalize text-to-image generation. Typical works focus on learning the new subject's private attributes. However, an important fact has not been taken seriously that a subject is not an isolated new concept but should be a specialization of a certain category in the pre-trained model. This results in the subject failing to comprehensively inherit the attributes in its category, causing poor attribute-related generations. In this paper, motivated by object-oriented programming, we model the subject as a derived class whose base class is its semantic category. This modeling enables the subject to inherit public attributes from its category while learning its private attributes from the user-provided example. Specifically, we propose a plug-and-play method, Subject-Derived regularization (SuDe). It constructs the base-derived class modeling by constraining the subject-driven generated images to semantically belong to the subject's category. Extensive experiments under three baselines and two backbones on various subjects show that our SuDe enables imaginative attribute-related generations while maintaining subject fidelity. For the codes, please refer to FaceChain.