# qianqianwang68/omnimotion

Repository: https://github.com/qianqianwang68/omnimotion
Canonical: https://ross.abutalabs.com/products/omnimotion
Language: Python
License: Apache-2.0
License Family: permissive
Last push: 2024-06-11T18:26:14+00:00

## Health v2 (maintenance only)
Score: 29/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 84
- inputs: {"age_days": 1181, "days_push": 813, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2268, forks 132 (observed 2026-08-28T04:06:32.884231+00:00)

## What it is
OmniMotion is a PyTorch implementation of the ICCV 2023 paper 'Tracking Everything Everywhere All at Once', which tracks every point in a video using a quasi-3D neural representation. It fits a per-video model that yields dense, global, long-range motion correspondences, depth, and occlusion information.

## Use cases
- track every point in a video across all frames
- compute dense long-range motion correspondences in video
- estimate pseudo-depth and occlusion for video sequences
- research video motion tracking with neural representations
- visualize point trails and correspondences in video
- reproduce ICCV 2023 paper results on video tracking

## When to choose
- you need dense, globally consistent point tracking across an entire video
- you want depth and occlusion estimates alongside motion tracking
- you are doing computer vision research on video motion and can afford GPU training time

## When to avoid
- you need real-time tracking - training takes 8-13 hours on a high-end GPU per sequence
- you want a plug-and-play pretrained tracker without per-video optimization
- you lack a CUDA-capable GPU or are on Windows/macOS without setup effort

## Facets
- artifact type: library
- maturity: maintenance
- function: computer-vision, machine-learning, deep-learning, image-processing, video-processing
- domain: computer-vision, machine-learning, deep-learning
- platform: python
- tags: optical-flow, point-tracking, neural-representation, quasi-3d, research-code, pytorch, iccv-2023, video, linux, gpu

## Member repositories
- qianqianwang68/omnimotion (main) score 29

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:32.884231+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 2026-08-30T02:42:02.590032+00:00, confidence not recorded.
  - readme: https://github.com/qianqianwang68/omnimotion (fetched 2026-08-28T04:06:32.884231+00:00, sha 85b19760bac6)
- Data as of 2026-08-30T08:39:29.467469+00:00.