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arXiv · 2603.06688

Narrative Weaver: Towards Controllable Long-Range Visual Consistency with Multi-Modal Conditioning

Zhengjian Yao·Yongzhi Li·Xinyuan Gao·Quan Chen·Peng Jiang·Yanye Lu
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Abstract

We present "Narrative Weaver", a novel framework that addresses a fundamental challenge in generative AI: achieving multi-modal controllable, long-range, and consistent visual content generation. While existing models excel at generating high-fidelity short-form visual content, they struggle to maintain narrative coherence and visual consistency across extended sequences - a critical limitation for real-world applications such as filmmaking and e-commerce advertising. Narrative Weaver introduces the first holistic solution that seamlessly integrates three essential capabilities: fine-grained control, automatic narrative planning, and long-range coherence. Our architecture combines a Multimodal Large Language Model (MLLM) for high-level narrative planning with a novel fine-grained control module featuring a dynamic Memory Bank that prevents visual drift. To enable practical deployment, we develop a progressive, multi-stage training strategy that efficiently leverages existing pre-trained models, achieving state-of-the-art performance even with limited training data. Recognizing the absence of suitable evaluation benchmarks, we construct and release the E-commerce Advertising Video Storyboard Dataset (EAVSD) - the first comprehensive dataset for this task, containing over 330K high-quality images with rich narrative annotations. Through extensive experiments across three distinct scenarios (controllable multi-scene generation, autonomous storytelling, and e-commerce advertising), we demonstrate our method's superiority while opening new possibilities for AI-driven content creation.

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Zhengjian Yao, Yongzhi Li, Xinyuan Gao, Quan Chen, Peng Jiang, Yanye Lu. 2026-03-13. Narrative Weaver: Towards Controllable Long-Range Visual Consistency with Multi-Modal Conditioning. https://arxiv.org/abs/2603.06688

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