NarraScore: Bridging Visual Narrative and Musical Dynamics via Hierarchical Affective Control
Synthesizing coherent soundtracks for long-form videos remains a formidable challenge, currently stalled by three critical impediments: computational scalability, temporal coherence, and, most critically, a pervasive semantic blindness to evolving narrative logic. To bridge these gaps, we propose NarraScore, a hierarchical framework predicated on the core insight that emotion serves as a high-density compression of narrative logic. Uniquely, we repurpose frozen Vision-Language Models (VLMs) as continuous affective sensors, distilling high-dimensional visual streams into dense, narrative-aware Valence-Arousal trajectories. Mechanistically, NarraScore employs a Dual-Branch Injection strategy to reconcile global structure with local dynamism: a Global Semantic Anchor ensures stylistic stability, while a surgical Token-Level Affective Adapter modulates local tension via direct element-wise residual injection. This minimalist design bypasses the bottlenecks of dense attention and architectural cloning, effectively mitigating the overfitting risks associated with data scarcity. Experiments demonstrate that NarraScore achieves state-of-the-art consistency and narrative alignment with negligible computational overhead, establishing a fully autonomous paradigm for long-video soundtrack generation.
698ebb55cace060ff123aec0
Yufan Wen
698ebb55cace060ff123aec1
Zhaocheng Liu
claimed_verified
2026-02-13T09:36:13.001Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/633e570be7d5ce7bfe037a53/zV8ULv4Mu7YIGZ8D3JtmK.jpeg
Zhaocheng Liu
zhaocheng
user
zhaocheng
698ebb55cace060ff123aec2
YeGuo Hua
698ebb55cace060ff123aec3
Ziyi Guo
698ebb55cace060ff123aec4
Lihua Zhang
698ebb55cace060ff123aec5
Chun Yuan
698ebb55cace060ff123aec6
Jian Wu
NarraScore: Bridging Visual Narrative and Musical Dynamics via Hierarchical Affective Control
Synthesizing coherent soundtracks for long-form videos remains a formidable challenge, currently stalled by three critical impediments: computational scalability, temporal coherence, and, most critically, a pervasive semantic blindness to evolving narrative logic. To bridge these gaps, we propose NarraScore, a hierarchical framework predicated on the core insight that emotion serves as a high-density compression of narrative logic. Uniquely, we repurpose frozen Vision-Language Models (VLMs) as continuous affective sensors, distilling high-dimensional visual streams into dense, narrative-aware Valence-Arousal trajectories. Mechanistically, NarraScore employs a Dual-Branch Injection strategy to reconcile global structure with local dynamism: a Global Semantic Anchor ensures stylistic stability, while a surgical Token-Level Affective Adapter modulates local tension via direct element-wise residual injection. This minimalist design bypasses the bottlenecks of dense attention and architectural cloning, effectively mitigating the overfitting risks associated with data scarcity. Experiments demonstrate that NarraScore achieves state-of-the-art consistency and narrative alignment with negligible computational overhead, establishing a fully autonomous paradigm for long-video soundtrack generation.
NarraScore presents a hierarchical framework that uses frozen Vision-Language Models as affective sensors to generate coherent soundtracks for long-form videos by combining global semantic anchors with token-level adaptive modulation.
Vision-Language Models Valence-Arousal trajectories Dual-Branch Injection Global Semantic Anchor Token-Level Affective Adapter residual injection overfitting risks