用事件相机拍视频，AI能补出高清慢动作
事件相机只记录像素亮度的变化，不拍完整帧，数据稀疏但极快。过去的方法要么模糊，要么长视频会跑偏。这篇把预训练的视频扩散模型拿来，通过自回归展开和自适应上下文切换，让AI在超长序列中保持稳定，还能做帧预测和插帧。在真实基准上，重建、预测、插帧三项任务都超过现有方法，且零样本泛化。它不是你明天能用上的，但意味着未来手机或无人机用事件相机拍出高清慢动作视频成为可能。
📄 原文摘要(英文)
Recovering high-quality video from sparse event streams is a challenging task. Regression methods often blur textures, while existing generative models struggle with long-term stability. We propose LongE2V, a novel approach that leverages pre-trained video diffusion priors to jointly handle event-based video reconstruction, prediction, and frame interpolation. By fine-tuning a foundational video model, our approach achieves high data efficiency and superior perceptual quality. We introduce Autoregressive Unrolling and Adaptive Context Switching to mitigate temporal drift in extremely long sequences. We also propose Reencoding Alignment with Cross Residual Correction to ensure precise bidirectional consistency during frame interpolation. Furthermore, Event Voxel Density Augmentation ensures robustness across varying sensor resolutions. Extensive experiments on real-world benchmarks demonstrate that LongE2V outperforms state-of-the-art methods across all three tasks, exhibiting exceptional temporal coherence and zero-shot generalization. Project page: https://cdfan0627.github.io/LongE2V-page/