# nfrechette/acl

Animation Compression Library

Repository: https://github.com/nfrechette/acl
Canonical: https://ross.abutalabs.com/products/nfrechette-acl
Language: C++
License: MIT
License Family: permissive
Topics: compression, animation-compression, game-engine, animation-3d, cpp, c-plus-plus, game-development
Last push: 2025-11-26T02:28:49+00:00

## Health v2 (maintenance only)
Score: 47/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 54, release rhythm 8, longevity 100
- inputs: {"age_days": 3395, "days_push": 281, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1589, forks 129 (observed 2026-08-28T04:05:08.113493+00:00)

## What it is
A production-ready C++11 header-only library for compressing skeletal animation clips with high compression ratios, fast decompression, and minimal accuracy loss. It also serves as a benchmark for comparing animation compression techniques.

## Use cases
- compress animation clips in a game engine
- reduce animation memory footprint at runtime
- speed up animation clip sampling and pose extraction
- benchmark animation compression algorithms
- integrate animation compression into a custom engine

## When to choose
- you need state-of-the-art animation compression with fast decompression
- you want a header-only C++11 library that is easy to integrate
- accuracy and cinematographic quality must not be compromised

## When to avoid
- you need runtime animation playback/skinning rather than compression
- your project is not C++ or does not target game/real-time workloads

## Facets
- artifact type: library
- maturity: active
- function: compression, graphics, game-engine
- domain: graphics, performance
- platform: cpp, cross-platform, game-engine
- tags: animation-compression, header-only, cpp11, runtime-decompression, character-animation, game-development, algorithms

## Member repositories
- nfrechette/acl (main) score 47

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:08.113493+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 2026-08-30T03:54:58.043083+00:00, confidence not recorded.
  - readme: https://github.com/nfrechette/acl (fetched 2026-08-28T04:05:08.113493+00:00, sha a399496f8e55)
- Data as of 2026-08-30T08:39:29.467469+00:00.