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Meta AI Releases Cotracker3: A Semi-Supervised Tracker that Produces Better Results with Unlabelled Data and Simple Architecture

Meta AI Releases Cotracker3: A Semi-Supervised Tracker that Produces Better Results with Unlabelled Data and Simple Architecture

Understanding Point Tracking in Video

Point tracking is essential for video tasks like 3D reconstruction and editing. It requires accurate point approximation for high-quality results. Recent advancements in tracking technology use transformer and neural network designs to track multiple points at once. However, these technologies need high-quality training data, which is often manually annotated.

The Challenge with Training Data

While there are many videos available for training, annotating points manually is time-consuming. Synthetic videos could help, but they are expensive and less effective than real videos. Unsupervised learning offers a promising solution to this issue.

Introducing Cotracker 3

Meta has developed Cotracker 3, a new tracking model that uses real videos without the need for manual annotations. It generates pseudo labels using existing models, simplifying the tracking process.

Key Benefits of Cotracker 3

  • Simplified Architecture: Cotracker 3 removes unnecessary components from previous models, making it smaller and more efficient.
  • Scalability: It addresses the challenges of scalability in unsupervised tracking.
  • Reduced Complexity: Unlike other models, it doesn’t require millions of training videos.

How Cotracker 3 Works

Cotracker 3 predicts point tracks for each video frame and provides visibility and confidence scores. Visibility indicates if a point is visible, while confidence measures how accurately the point is tracked.

Versions of Cotracker 3

There are two versions of Cotracker 3:

  • Online Version: Processes video sequentially, tracking points in real-time.
  • Offline Version: Analyzes the entire video at once for tracking.

Training and Performance

For training, Cotracker 3 used around 100,000 videos and several teacher models trained on synthetic data. It employs convolutional networks to extract features and calculate correlations, making it efficient and effective.

Advantages Over Other Trackers

  • Lean and Fast: Cotracker 3 has fewer parameters and is faster than its predecessors.
  • Competitive Results: It performs well against other trackers and sometimes exceeds state-of-the-art models.

The Power of Simplicity

Cotracker 3 combines the best features of previous models into a simpler, more effective package. Its semi-supervised training method uses unannotated videos, demonstrating that simplicity can lead to better performance.

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