title: Tracktention: Leveraging Point Tracking to Attend Videos Faster and
    Better
summary: Temporal consistency is critical in video prediction to ensure that outputs
  are coherent and free of artifacts. Traditional methods, such as temporal
  attention and 3D convolution, may struggle with significant object motion and
  may not capture long-range temporal dependencies in dynamic scenes. To address
  this gap, we propose the Tracktention Layer, a novel architectural component
  that explicitly integrates motion information using point tracks, i.e.,
  sequences of corresponding points across frames. By incorporating these motion
  cues, the Tracktention Layer enhances temporal alignment and effectively
  handles complex object motions, maintaining consistent feature representations
  over time. Our approach is computationally efficient and can be seamlessly
  integrated into existing models, such as Vision Transformers, with minimal
  modification. It can be used to upgrade image-only models to state-of-the-art
  video ones, sometimes outperforming models natively designed for video
  prediction. We demonstrate this on video depth prediction and video
  colorization, where models augmented with the Tracktention Layer exhibit
  significantly improved temporal consistency compared to baselines.
paper_authors._id: 67e6ec0188bae9650ccefa02
paper_authors.name: Zihang Lai
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-31T08:21:29.995Z
paper_authors._id: 64b6fc043240387159385543
paper_authors.avatarUrl: /avatars/a63c688d176758ee826b3d02c11db8a9.svg
paper_authors.fullname: Zihang Lai
paper_authors.type: user
paper_authors.user: zlai
paper_authors._id: 67e6ec0188bae9650ccefa03
paper_authors.name: Andrea Vedaldi
paper_title: Tracktention: Leveraging Point Tracking to Attend Videos Faster and
    Better
paper_summary: Temporal consistency is critical in video prediction to ensure that outputs
  are coherent and free of artifacts. Traditional methods, such as temporal
  attention and 3D convolution, may struggle with significant object motion and
  may not capture long-range temporal dependencies in dynamic scenes. To address
  this gap, we propose the Tracktention Layer, a novel architectural component
  that explicitly integrates motion information using point tracks, i.e.,
  sequences of corresponding points across frames. By incorporating these motion
  cues, the Tracktention Layer enhances temporal alignment and effectively
  handles complex object motions, maintaining consistent feature representations
  over time. Our approach is computationally efficient and can be seamlessly
  integrated into existing models, such as Vision Transformers, with minimal
  modification. It can be used to upgrade image-only models to state-of-the-art
  video ones, sometimes outperforming models natively designed for video
  prediction. We demonstrate this on video depth prediction and video
  colorization, where models augmented with the Tracktention Layer exhibit
  significantly improved temporal consistency compared to baselines.
paper_ai_summary: The Tracktention Layer, which uses point tracks to integrate motion information, enhances temporal consistency and efficiency in video prediction tasks like depth estimation and colorization.
paper_ai_keywords: Temporal attention 3D convolution Tracktention Layer point tracks temporal alignment Vision Transformers video prediction video depth prediction video colorization