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

2026-04-23 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision & Pattern Recognition · Depth: Expert, quick

Summary

Researchers have developed models to perceive and control the passage of time in videos, addressing the challenge of detecting speed changes and estimating playback speed. The approach leverages multimodal cues and temporal structures within videos, learning in a self-supervised manner. This capability enabled the curation of the largest slow-motion video dataset from noisy, in-the-wild sources, which provides richer temporal detail than standard footage. Utilizing this dataset, the team further developed models for temporal control, including speed-conditioned video generation that produces motion at specified playback speeds, and temporal super-resolution, which converts low-FPS, blurry videos into high-FPS sequences with fine-grained temporal details. This work positions time as a manipulable perceptual dimension in video learning.

Key takeaway

For research scientists developing video generation or analysis systems, understanding time as a learnable visual concept is crucial. Your models can be enhanced by incorporating self-supervised temporal reasoning to detect speed changes and generate speed-conditioned video. Consider leveraging high-detail slow-motion datasets to improve temporal super-resolution capabilities, leading to more realistic and controllable video outputs.

Key insights

Time can be learned as a visual concept for video speed detection, generation, and super-resolution.

Principles

• Multimodal cues aid self-supervised temporal learning.
• Slow-motion data enriches temporal detail perception.

Method

Self-supervised learning detects speed changes and estimates playback speed, then curates slow-motion data for speed-conditioned video generation and temporal super-resolution.

In practice

• Generate videos at specific playback speeds.
• Convert low-FPS video to high-FPS detail.
• Detect manipulated video playback speed.

Topics

• Video Time Perception
• Playback Speed Estimation
• Self-supervised Video Learning
• Slow-motion Video Dataset
• Temporal Super-resolution

Best for: Research Scientist, AI Scientist, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.