[2603.02816] BrandFusion: A Multi-Agent Framework for Seamless Brand Integration in Text-to-Video Generation

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.02816 (cs) [Submitted on 3 Mar 2026] Title:BrandFusion: A Multi-Agent Framework for Seamless Brand Integration in Text-to-Video Generation Authors:Zihao Zhu, Ruotong Wang, Siwei Lyu, Min Zhang, Baoyuan Wu View a PDF of the paper titled BrandFusion: A Multi-Agent Framework for Seamless Brand Integration in Text-to-Video Generation, by Zihao Zhu and 4 other authors View PDF HTML (experimental) Abstract:The rapid advancement of text-to-video (T2V) models has revolutionized content creation, yet their commercial potential remains largely untapped. We introduce, for the first time, the task of seamless brand integration in T2V: automatically embedding advertiser brands into prompt-generated videos while preserving semantic fidelity to user intent. This task confronts three core challenges: maintaining prompt fidelity, ensuring brand recognizability, and achieving contextually natural integration. To address them, we propose BrandFusion, a novel multi-agent framework comprising two synergistic phases. In the offline phase (advertiser-facing), we construct a Brand Knowledge Base by probing model priors and adapting to novel brands via lightweight fine-tuning. In the online phase (user-facing), five agents jointly refine user prompts through iterative refinement, leveraging the shared knowledge base and real-time contextual tracking to ensure brand visibility and semantic alignment. Experiments on 18 established...