用于快速红外与可见光视频融合的流式扩散模型
Streaming Diffusion Model for Fast Infrared and Visible Video Fusion
将预训练扩散模型的生成先验蒸馏到单步采样框架，并用记忆增强的潜变量管道与时序聚合适配器保证视频帧间一致性，为视频复原/融合任务提供高效且时序稳定的扩散方案。
原文依据 we propose a streaming diffusion model for efficient infrared and visible video fusion, termed SDMFusion
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论红外与可见光视频融合对于鲁棒感知系统至关重要，旨在合成一个综合视频流，同时利用热红外鲁棒性和纹理细节。然而，现有方法将视频视为独立帧序列，本质上引入了时序不一致性，如闪烁和鬼影伪影。尽管扩散模型具有强大的生成先验来弥补这一点，但其迭代特性对于视频而言过于缓慢。为了解决这一根本性困境，我们提出了一种用于高效红外与可见光视频融合的流式扩散模型，称为SDMFusion。我们的关键见解是利用预训练扩散模型的生成先验进入单步采样框架，同时显式建模时序动态。我们设计了一个记忆增强的潜变量管道，其中时序聚合适配器对齐并传播跨帧特征以确保一致性，并辅以专门的时序一致性损失。该方法有效地将实现高保真度的挑战与保持时序稳定性解耦。在四个基准上的大量实验表明，我们的方法建立了新的最先进水平，以适合实时应用的速度生成具有卓越时空一致性的融合视频。代码可在 https://github.com/DandanYoung/SDMFusion 获取。
英文原文摘要 1392 字符
Infrared and visible video fusion is pivotal for robust perceptual systems, aiming to synthesize a comprehensive video stream that leverages both thermal resilience and textured details. However, prevailing methods, by treating videos as sequences of independent frames, inherently introduce temporal incoherence, such as flickering and ghosting artifacts. While diffusion models possess strong generative priors to remedy this, their iterative nature is prohibitively slow for video. To resolve this fundamental dilemma, we propose a streaming diffusion model for efficient infrared and visible video fusion, termed SDMFusion. Our key insight is to exploit the generative prior of a pre-trained diffusion model into a one-step sampling framework, while explicitly modeling temporal dynamics. We design a memory-augmented latent pipeline where a temporal aggregation adapter aligns and propagates cross-frame features to ensure coherence, supported by a dedicated temporal consistency loss. This approach effectively decouples the challenge of achieving high fidelity from maintaining temporal stability. Extensive experiments on four benchmarks demonstrate that our method establishes a new state-of-the-art, generating fused videos with exceptional spatio-temporal consistency at a speed suitable for real-time application. The code is available at https://github.com/DandanYoung/SDMFusion.