# ubisoft/ubisoft-laforge-animation-dataset

Ubisoft La Forge Animation Dataset

Repository: https://github.com/ubisoft/ubisoft-laforge-animation-dataset
Canonical: https://ross.abutalabs.com/products/ubisoft-laforge-animation-dataset
Language: Python
License: NOASSERTION
License Family: other
Topics: character-animation, motion-capture-data
Last push: 2022-09-10T09:16:51+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2332, "days_push": 1453, "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 1568, forks 327 (observed 2026-08-28T04:05:05.100804+00:00)

## What it is
The Ubisoft La Forge Animation Dataset (LAFAN1) is a motion capture dataset of 5 subjects, 77 sequences, and ~4.6 hours of character animation data in BVH format, released alongside code for the SIGGRAPH 2020 'Robust Motion In-betweening' paper. It includes benchmarking code for evaluating motion in-betweening models and is licensed under CC BY-NC-ND 4.0.

## Use cases
- train deep learning models for character motion in-betweening
- benchmark motion transition generation algorithms
- research data-driven character animation and motion matching
- train physics-based character controllers from mocap data
- denoise or retarget optical motion capture data
- study locomotion and dance motion sequences for animation research

## When to choose
- you need a large, high-quality mocap dataset for character animation research
- you want to reproduce or compare against published motion in-betweening results
- you need BVH-format motion data for motion matching or learned motion synthesis
- you are doing academic research on data-driven character animation

## When to avoid
- you need a permissively licensed dataset for commercial products (CC BY-NC-ND forbids commercial use and derivatives)
- you need real-time animation tooling rather than raw data and research code
- you need mocap of objects, faces, or animals rather than human characters
- you cannot handle large files via git LFS

## Facets
- artifact type: dataset
- maturity: stable
- function: machine-learning, animation, benchmarking, data-generation
- domain: machine-learning, computer-vision, graphics
- platform: cross-platform, python
- tags: motion-capture, character-animation, bvh, motion-in-betweening, deep-learning, siggraph, game-development, research

## Member repositories
- ubisoft/ubisoft-laforge-animation-dataset (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:05.100804+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 2026-08-30T03:58:58.617311+00:00, confidence not recorded.
  - readme: https://github.com/ubisoft/ubisoft-laforge-animation-dataset (fetched 2026-08-28T04:05:05.100804+00:00, sha e670e71b50f6)
- Data as of 2026-08-30T08:39:29.467469+00:00.