💡 The paper proposes a framework called HOMIE for human-object centric video personalization, which is a core task in subject-driven video generation. Existing methods suffer from two key limitations: they struggle to balance high subject fidelity and accurate interaction patterns between humans and diverse objects, and they lack mechanisms to understand latent correspondence in intra-subject references. To address these challenges, HOMIE tackles both inter- and intra-subject input settings in a unified manner. The framework proposes a better integration strategy to extract knowledge of reference-level relationships without compromising the controllability of text encoders or incurring costly re-alignment. Specifically, it introduces global multimodal guidance within self-attention to better align semantic features derived from large language models with tokens from variational autoencoders. Additionally, it proposes modality-reference embedding to differentiate tokens from large language model features and variational autoencoder tokens and associate intra-subject reference image tokens. Extensive experiments validate that the method achieves state-of-the-art performance across various human-object centric video personalization tasks. The results demonstrate the effectiveness of HOMIE in addressing the limitations of existing methods and providing a unified framework for video personalization.