Seeing Fast and Slow: Learning the Flow of Time in Videos

## 📄 2604.21931v1 **作者**: Yen-Siang Wu, Rundong Luo, Jingsen Zhu, Tao Tu, Ali Farhadi **分类**: cs.CV, cs.AI, cs.GR **发表**: 2026-04-23 ### 摘要 How can we tell whether a video has been sped up or slowed down? How can we generate videos at different speeds? Although videos have been central to modern computer vision research, little attention has been paid to perceiving and controlling the passage of time. In this paper, we study time as a learnable visual concept and develop models for reasoning about and manipulating the flow of time in videos. We first exploit the multimodal cues and temporal structure naturally present in videos to learn, in a self-supervised manner, to detect speed changes and estimate playback speed. We then show that these learned temporal reasoning models enable us to curate the largest slow-motion video dataset to date from noisy in-the-wild sources. Such slow-motion footage, typically filmed by high-speed cameras, contains substantially richer temporal detail than standard videos. Using this data, we further develop models capable of temporal control, including speed-conditioned video generation, which produces motion at specified playback speed, and temporal super-resolution, which tranforms low-FPS, blurry videos into high-FPS sequences with fine-grained temporal details. Our findings highlight time as a manipulable, perceptual dimension in video learning, opening doors to temporally controllable video generation, temporal forensics detection, and potentially richer world-models that understand how events unfold over time. 🔗 arXiv 论文页面 --- 这篇论文把"时间"本身当成了可学习的视觉特征，有点意思——让模型自己学会感知视频是快进还是慢放，甚至反过来生成不同速度的视频。自监督学习加上大规模慢动作数据集，这条路走通的话，视频生成和世界模型都会受益匪浅。

<< 返回首页