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

2026-03-03

AI Summary

This work introduces and addresses the novel problem of seamless brand integration in text-to-video generation, aiming to simultaneously preserve semantic fidelity to user intent, ensure brand recognizability, and achieve natural contextual blending. To this end, the authors propose BrandFusion, a multi-agent framework that constructs an offline brand knowledge base and orchestrates five online agents to iteratively refine generation prompts. The framework integrates lightweight fine-tuned brand prior detectors with context-aware embedding mechanisms to harmonize brand elements within dynamic video content. Extensive experiments demonstrate that BrandFusion significantly outperforms existing baselines across 18 mainstream and 2 custom brands, achieving state-of-the-art performance in semantic fidelity, brand identifiability, contextual naturalness, and human evaluation scores.

Technology Category

• Natural Language Processing: Generation
• Computer Vision: Video Understanding & Activity Analysis
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Agentic search
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

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 and 2 custom brands across multiple state-of-the-art T2V models demonstrate that BrandFusion significantly outperforms baselines in semantic preservation, brand recognizability, and integration naturalness. Human evaluations further confirm higher user satisfaction, establishing a practical pathway for sustainable T2V monetization.

Problem

• brand integration
• text-to-video generation
• semantic fidelity
• brand recognizability
• contextual naturalness

Innovation

• multi-agent framework
• brand integration
• text-to-video generation
• prompt refinement
• brand knowledge base