VideoFusion: A Spatio-Temporal Collaborative Network for Mutli-modal Video Fusion and Restoration

• URL: http://arxiv.org/abs/2503.23359v1
• Date: Sun, 30 Mar 2025 08:27:18 GMT
• Title: VideoFusion: A Spatio-Temporal Collaborative Network for Mutli-modal Video Fusion and Restoration
• Authors: Linfeng Tang, Yeda Wang, Meiqi Gong, Zizhuo Li, Yuxin Deng, Xunpeng Yi, Chunyu Li, Han Xu, Hao Zhang, Jiayi Ma,
• Abstract summary: Existing multi-sensor fusion research predominantly integrates complementary from multiple images rather than videos.<n>VideoFusion exploits cross-modal complementarity and temporal dynamics to generate context-temporally coherent videos.<n>Extensive experiments reveal that VideoFusion outperforms existing image-oriented fusion paradigms in sequential scenarios.
• Score: 26.59510171451438
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Compared to images, videos better align with real-world acquisition scenarios and possess valuable temporal cues. However, existing multi-sensor fusion research predominantly integrates complementary context from multiple images rather than videos. This primarily stems from two factors: 1) the scarcity of large-scale multi-sensor video datasets, limiting research in video fusion, and 2) the inherent difficulty of jointly modeling spatial and temporal dependencies in a unified framework. This paper proactively compensates for the dilemmas. First, we construct M3SVD, a benchmark dataset with $220$ temporally synchronized and spatially registered infrared-visible video pairs comprising 153,797 frames, filling the data gap for the video fusion community. Secondly, we propose VideoFusion, a multi-modal video fusion model that fully exploits cross-modal complementarity and temporal dynamics to generate spatio-temporally coherent videos from (potentially degraded) multi-modal inputs. Specifically, 1) a differential reinforcement module is developed for cross-modal information interaction and enhancement, 2) a complete modality-guided fusion strategy is employed to adaptively integrate multi-modal features, and 3) a bi-temporal co-attention mechanism is devised to dynamically aggregate forward-backward temporal contexts to reinforce cross-frame feature representations. Extensive experiments reveal that VideoFusion outperforms existing image-oriented fusion paradigms in sequential scenarios, effectively mitigating temporal inconsistency and interference.

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