Unified Personalized Understanding, Generating and Editing
Yu Zhong, Tianwei Lin, Ruike Zhu, Yuqian Yuan, Haoyu Zheng, Liang Liang, Wenqiao Zhang, Feifei Shao, Haoyuan Li, Wanggui He, Hao Jiang, Yueting Zhuang

TL;DR
OmniPersona is an end-to-end framework for personalized multimodal understanding, generation, and editing, addressing limitations of previous methods by decoupling tasks and propagating personalized knowledge.
Contribution
It introduces structurally decoupled concept tokens and a knowledge replay mechanism for unified personalized multimodal tasks, a novel approach in the field.
Findings
Achieves robust performance across diverse personalization tasks
Effectively propagates personalized attribute knowledge across tasks
Sets a new baseline for unified personalized multimodal models
Abstract
Unified large multimodal models (LMMs) have achieved remarkable progress in general-purpose multimodal understanding and generation. However, they still operate under a ``one-size-fits-all'' paradigm and struggle to model user-specific concepts (e.g., generate a photo of \texttt{<maeve>}) in a consistent and controllable manner. Existing personalization methods typically rely on external retrieval, which is inefficient and poorly integrated into unified multimodal pipelines. Recent personalized unified models introduce learnable soft prompts to encode concept information, yet they either couple understanding and generation or depend on complex multi-stage training, leading to cross-task interference and ultimately to fuzzy or misaligned personalized knowledge. We present \textbf{OmniPersona}, an end-to-end personalization framework for unified LMMs that, for the first time, integrates…
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Taxonomy
TopicsMultimodal Machine Learning Applications · Generative Adversarial Networks and Image Synthesis · Image Retrieval and Classification Techniques
