# HumanAIGC/AnimateAnyone

Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation

Repository: https://github.com/HumanAIGC/AnimateAnyone
Canonical: https://ross.abutalabs.com/products/animateanyone
License: Apache-2.0
License Family: permissive
Last push: 2025-09-20T13:56:23+00:00

## Health v2 (maintenance only)
Score: 46/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 43, release rhythm 35, longevity 72
- inputs: {"age_days": 1009, "days_push": 347, "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 14791, forks 1005 (observed 2026-08-28T04:11:07.693031+00:00)

## What it is
Animate Anyone is the official research implementation of a diffusion-based image-to-video synthesis method that animates a static character image using pose sequences for consistent, controllable character animation. It is a research codebase accompanying an arXiv paper from Alibaba's HumanAIGC team.

## Use cases
- animate a character photo from a pose video
- generate character animation from a single image
- image-to-video synthesis research
- drive a static character with motion capture poses
- create virtual human dance videos
- reproduce the Animate Anyone paper

## When to choose
- you need a reference implementation of pose-driven character animation from a single image
- you are doing research on controllable image-to-video diffusion models
- you want to animate character images with pose sequences on GPU hardware

## When to avoid
- you need a production-ready, polished end-user application
- you lack a capable GPU for diffusion model inference
- you want turnkey video generation without model setup and weights

## Facets
- artifact type: library
- maturity: active
- function: video-processing, machine-learning, deep-learning, image-processing
- domain: artificial-intelligence, computer-vision, image-processing, deep-learning
- platform: python
- tags: image-to-video, character-animation, diffusion-models, pose-driven-animation, research-code, generative-ai, video, gpu, linux

## Member repositories
- HumanAIGC/AnimateAnyone (main) score 46

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:07.693031+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 2026-08-29T17:07:09.687921+00:00, confidence not recorded.
  - readme: https://github.com/HumanAIGC/AnimateAnyone (fetched 2026-08-28T04:11:07.693031+00:00, sha e5ccfac8fbae)
- Data as of 2026-08-30T08:39:29.467469+00:00.