titleLongE2V: Long-Horizon Event-based Video Reconstruction, Prediction, and Frame Interpolation with Video Diffusion Models
authorsCheng-De Fan, Chun-Wei Tuan Mu, Chen-Wei Chang, Chin-Yang Lin, Kun-Ru Wu, Yu-Chee Tseng, Yu-Lun Liu
date2026-07-09
venuearXiv (preprint, not peer reviewed)
idarXiv:2607.08770
affiliationNational Yang Ming Chiao Tung University
upvotes33 (Hugging Face Daily Papers, 2026-11-01)
linksabs pdf hf code
abstractRecovering 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/

cite

@misc{fan2026longe,
  title={LongE2V: Long-Horizon Event-based Video Reconstruction, Prediction, and Frame Interpolation with Video Diffusion Models},
  author={Cheng-De Fan and Chun-Wei Tuan Mu and Chen-Wei Chang and Chin-Yang Lin and Kun-Ru Wu and Yu-Chee Tseng and Yu-Lun Liu},
  year={2026},
  eprint={2607.08770},
  archivePrefix={arXiv},
  url={https://arxiv.org/abs/2607.08770}
}