BrandFusion: A Multi-Agent Framework for Seamless Brand Integration in Text-to-Video Generation
Proposes BrandFusion, a multi-agent framework for seamless brand embedding in text-to-video, improving recognizability and naturalness.
Key Findings
Methodology
The approach combines offline knowledge base construction with online multi-agent collaboration. Offline, probing and lightweight fine-tuning build brand recognition capabilities. Online, five specialized agents—brand selector, strategy generator, prompt refiner, critic, and experience learner—work iteratively to optimize prompts, leveraging large pre-trained models like GPT-5. The system balances semantic fidelity, brand visibility, and scene naturalness through multi-round feedback, real-time context tracking, and knowledge retrieval, enabling adaptive, high-quality brand embedding in diverse videos.
Key Results
- On 18 established and 2 novel brands, the framework outperforms baselines with an average VBench-Quality increase of 0.02, CLIPScore up by 0.05, and brand presence rate reaching 94.7%. Across multiple models (Veo, Sora, Kling), it maintains high semantic fidelity and naturalness, especially in low-match scenarios, demonstrating robustness.
- Human evaluation scores averaged 4.4/5, confirming higher user satisfaction compared to baseline methods. The system effectively adapts to unseen brands via lightweight fine-tuning with LoRA, achieving high visual fidelity and seamless scene integration, validating its generalization capability.
- Ablation studies show that richer brand knowledge bases and more iterative refinement rounds significantly improve recognition and naturalness, highlighting the importance of multi-agent collaboration and knowledge management.
Significance
This work addresses a critical bottleneck in commercial content creation—integrating brands naturally into videos while preserving user intent. It advances the state-of-the-art in multimodal content generation, offering scalable, adaptable solutions for advertising, virtual try-on, and immersive media. By systematically balancing semantic accuracy, recognizability, and scene harmony, it paves the way for more engaging, less intrusive branded content, fostering sustainable monetization and user engagement in multimedia industries.
Technical Contribution
The paper introduces a novel multi-agent framework combining offline knowledge probing and lightweight fine-tuning with online collaborative prompt refinement. It leverages large-scale pre-trained models (e.g., GPT-5) for multi-round optimization, integrating knowledge bases, adaptive strategies, and real-time feedback. This approach surpasses traditional single-pass prompt methods, providing a scalable, generalizable architecture for multimodal content synthesis with high semantic fidelity and natural scene embedding.
Novelty
This is the first work to formalize seamless brand integration in text-to-video generation, employing a multi-agent system that systematically constructs brand knowledge and dynamically refines prompts. Unlike prior work focused on implicit or covert brand injection, this approach emphasizes explicit, natural embedding with high recognizability and scene harmony, addressing both technical and commercial challenges in a unified framework.
Limitations
- The system's performance diminishes in highly dynamic, complex scenes with fast movements, due to limited contextual understanding. Handling such scenarios requires further model enhancements.
- The knowledge base size and diversity limit adaptation to emerging or niche brands, necessitating larger, more diverse datasets and continual updating.
- Computational costs of multi-agent collaboration and large model inference pose challenges for real-time deployment, especially at scale. Future work should focus on efficiency improvements.
Future Work
Future directions include optimizing multi-agent efficiency, integrating reinforcement learning for adaptive strategy selection, expanding multimodal knowledge bases, and supporting multi-brand simultaneous embedding. Additionally, exploring real-time deployment in AR/VR environments and user feedback loops will further enhance system robustness and commercial viability.
AI Executive Summary
The rapid evolution of text-to-video (T2V) technology has transformed content creation, enabling high-fidelity video synthesis from natural language prompts. Leading models like Veo, Sora, and Kling have demonstrated remarkable capabilities, but embedding recognizable brands into generated videos remains a significant challenge. Traditional methods often rely on simple prompt concatenation or post-processing, which lack naturalness and robustness, especially across diverse scenarios.
To address this, the authors propose BrandFusion, a sophisticated multi-agent framework that systematically integrates brands into videos while preserving semantic fidelity. The system operates in two phases: offline knowledge base construction and online collaborative prompt refinement. During offline, probing and lightweight fine-tuning establish a comprehensive brand knowledge repository. In the online phase, five specialized agents—brand selector, strategy generator, prompt refiner, critic, and experience learner—work iteratively to optimize prompts, leveraging large pre-trained models like GPT-5. This multi-layered approach ensures that brand elements are both clearly visible and seamlessly embedded within scene contexts.
Extensive experiments across 18 well-known brands and 2 emerging brands demonstrate the framework’s effectiveness. Quantitative metrics such as VBench-Quality, CLIPScore, and brand recognition rates show significant improvements over baseline methods. Human evaluations further confirm higher user satisfaction, with scores averaging 4.4 out of 5. The system maintains high semantic fidelity and naturalness even in challenging low-match scenarios, validating its robustness and adaptability.
This research offers a promising pathway for commercial applications, including advertising, virtual try-on, and immersive media. By enabling natural, recognizable brand embedding, it reduces user disruption and enhances brand exposure organically. While current limitations include computational costs and scene complexity handling, future work aims to optimize efficiency and expand capabilities, pushing the boundaries of multimodal content generation and commercialization.
Deep Dive
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.