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

2026-04-23 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision · Depth: Expert, medium

Summary

Researchers have developed models to perceive and control the passage of time in videos, addressing the underexplored concept of time as a learnable visual dimension. The approach leverages multimodal cues and temporal structures in videos to self-supervise learning for detecting speed changes and estimating playback speed. These models were used to curate the largest slow-motion video dataset from noisy, in-the-wild sources, which provides richer temporal detail than standard videos. Utilizing this dataset, the team further created models for temporal control, including speed-conditioned video generation that produces motion at specified playback speeds, and temporal super-resolution, which transforms low-FPS, blurry videos into high-FPS sequences with fine-grained details. This work, published on April 23, 2026, opens new avenues for temporally controllable video generation and temporal forensics.

Key takeaway

For research scientists developing video generation or analysis systems, this work highlights the importance of explicitly modeling time as a perceptual dimension. You should consider integrating temporal reasoning capabilities to enable more sophisticated video manipulation, such as precise speed control and temporal super-resolution, which can lead to richer world-models and enhanced forensic tools.

Key insights

Learning time as a visual concept enables video speed detection, temporal super-resolution, and speed-conditioned video generation.

Principles

• Multimodal cues aid self-supervised temporal learning.
• High-speed footage offers richer temporal detail.

Method

The method involves self-supervised learning from multimodal cues to detect speed changes, curating a slow-motion dataset, and then training models for speed-conditioned generation and temporal super-resolution.

In practice

• Generate videos at specified playback speeds.
• Transform low-FPS video into high-FPS sequences.
• Detect manipulated video playback speeds.

Topics

• Video Temporal Understanding
• Self-supervised Learning
• Slow-motion Video Dataset
• Speed-conditioned Video Generation
• Temporal Super-resolution

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

Related on AIssential

• Seeing Fast and Slow: Learning the Flow of Time in Videos — Time can be learned as a visual concept for video speed detection, generation, and super-resolution.
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• Towards One-to-Many Temporal Grounding — One-to-Many Temporal Grounding (OMTG) addresses localizing multiple video segments for a single query, overcoming MLLM limitations.
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• You Don't Need Strong Assumptions: Visual Representation Learning via Temporal Differences — Temporal Difference in Vision (TDV) learns visual representations from video by predicting future frames from past motion, eliminating strong inductive biases.
• Frozen Forecasting: A Unified Evaluation — Perceptual ability in frozen video models strongly correlates with their generalist forecasting performance over short time horizons.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.