# facebookresearch/co-tracker

CoTracker is a model for tracking any point (pixel) on a video.

Repository: https://github.com/facebookresearch/co-tracker
Canonical: https://ross.abutalabs.com/products/co-tracker
Homepage: https://co-tracker.github.io/
Language: Jupyter Notebook
License: NOASSERTION
License Family: other
Topics: optical-flow, point-tracking, track-anything
Last push: 2026-03-03T21:51:22+00:00

## Health v2 (maintenance only)
Score: 60/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 70, release rhythm 35, longevity 81
- inputs: {"age_days": 1147, "days_push": 183, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 5080, forks 385 (observed 2026-08-28T04:09:09.491827+00:00)

## What it is
CoTracker is a transformer-based model from Meta AI and Oxford VGG that jointly tracks any point (pixel) across a video, handling occlusions and long-term tracking. It provides pretrained checkpoints, training code, a pseudo-labeling pipeline, and demo notebooks (Colab, Hugging Face Space).

## Use cases
- track any pixel through a video
- track a quasi-dense grid of points jointly across frames
- track points through occlusions and out-of-view motion
- run online (causal) point tracking on video streams
- train or fine-tune a point tracking model with pseudo-labelled real videos
- use point tracking for downstream 3D reconstruction or motion analysis

## When to choose
- you need state-of-the-art point tracking in videos with occlusion handling
- you want to jointly track many points rather than independently
- you need pretrained checkpoints plus training code for research
- you want a fast transformer-based tracker suitable for online use

## When to avoid
- you only need simple frame-to-frame optical flow rather than long-term point tracks
- you need a production-ready application with a UI rather than a research model
- you lack GPU resources for inference or training
- you need a permissively licensed model for commercial use without checking the custom license

## Facets
- artifact type: library
- maturity: active
- function: computer-vision, machine-learning, deep-learning, video-processing
- domain: computer-vision, machine-learning, artificial-intelligence
- platform: python, cross-platform
- tags: point-tracking, optical-flow, transformer, video-understanding, pytorch, research-model, meta-ai, video, gpu

## Member repositories
- facebookresearch/co-tracker (main) score 60

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:09.491827+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:02:35.038925+00:00, confidence not recorded.
  - readme: https://github.com/facebookresearch/co-tracker (fetched 2026-08-28T04:09:09.491827+00:00, sha e5f1bc776397)
  - homepage: https://co-tracker.github.io/ (fetched 2026-08-29T08:57:23.644260+00:00, sha e0062e6a9d6c)
- Data as of 2026-08-30T08:39:29.467469+00:00.