Improving Personalized Image Generation through Social Context Feedback

• URL: http://arxiv.org/abs/2507.16095v1
• Date: Mon, 21 Jul 2025 22:36:30 GMT
• Title: Improving Personalized Image Generation through Social Context Feedback
• Authors: Parul Gupta, Abhinav Dhall, Thanh-Toan Do,
• Abstract summary: We propose to overcome these shortcomings through feedback-based fine-tuning of existing personalized generation methods.<n>State-of-art detectors of pose, human-object-interaction, human facial recognition and human gaze-point estimation are used to refine the diffusion model.<n>The images generated in this manner show an improvement in the generated interactions, facial identities and image quality over three benchmark datasets.
• Score: 15.582260415127935
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Personalized image generation, where reference images of one or more subjects are used to generate their image according to a scene description, has gathered significant interest in the community. However, such generated images suffer from three major limitations -- complex activities, such as $<$man, pushing, motorcycle$>$ are not generated properly with incorrect human poses, reference human identities are not preserved, and generated human gaze patterns are unnatural/inconsistent with the scene description. In this work, we propose to overcome these shortcomings through feedback-based fine-tuning of existing personalized generation methods, wherein, state-of-art detectors of pose, human-object-interaction, human facial recognition and human gaze-point estimation are used to refine the diffusion model. We also propose timestep-based inculcation of different feedback modules, depending upon whether the signal is low-level (such as human pose), or high-level (such as gaze point). The images generated in this manner show an improvement in the generated interactions, facial identities and image quality over three benchmark datasets.

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