Personalized Visual Content Generation in Conversational Systems
Xianquan Wang, Zhaocheng Du, Huibo Xu, Shukang Yin, Yupeng Han, Jieming Zhu, Kai Zhang, Qi Liu
With the rapid progress of large language models (LLMs) and diffusion models, there has been growing interest in personalized content generation. However, current conversational systems often present the same recommended content to all users, falling into the dilemma of "one-size-fits-all." To break this limitation and boost user engagement, in this paper, we introduce PiViC (**P**ersonal**i**zed **Vi**sual **C**ontent) generation, a unified framework for personalizing item images within conversational systems. We tackle two key bottlenecks: the depth of personalization and the fidelity of generated images. Speficially, an LLM-powered *Inclinations Analyzer* is adopted to capture user likes and dislikes from context to construct personalized prompts. Moreover, we design a dual-stage LoRA mechanism—*Global LoRA* for understanding task-specific visual style, and *Local LoRA* for capturing preferred visual elements from conversation history. During training, we introduce the visual content condition method to ensure LoRA learns both historical visual context and maintains fidelity to the original item images.Extensive experiments on benchmark conversational datasets—including objective metrics and GPT-based evaluations—demonstrate that our framework significantly outperforms strong baselines, which highlight its potential to redefine personalization in visual content generation for e-commerce.The code is available.