# TMElyralab/MusePose

MusePose: a Pose-Driven Image-to-Video Framework for Virtual Human Generation

Repository: https://github.com/TMElyralab/MusePose
Canonical: https://ross.abutalabs.com/products/musepose
Language: Python
License: NOASSERTION
License Family: other
Last push: 2025-03-05T11:37:41+00:00

## Health v2 (maintenance only)
Score: 28/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 9, release rhythm 35, longevity 59
- inputs: {"age_days": 831, "days_push": 546, "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 2701, forks 205 (observed 2026-08-28T04:07:11.426152+00:00)

## What it is
MusePose is a diffusion-based, pose-guided image-to-video generation framework for creating virtual human videos, where a character in a reference image is animated according to a given pose sequence. It is part of Tencent Music's Muse open-source series alongside MuseV and MuseTalk, and includes training code and a pose alignment algorithm.

## Use cases
- generate dance videos from a reference image and pose sequence
- animate a virtual human character from a single photo
- align arbitrary dance videos to reference images for better inference
- train a pose-driven video generation model
- build end-to-end virtual human generation pipelines
- reproduce or extend AnimateAnyone research

## When to choose
- you need pose-controlled human video generation from a still image
- you want an open-source implementation of AnimateAnyone with training code
- you are building virtual human or digital avatar pipelines
- you need high-quality dance video synthesis from open models

## When to avoid
- you need general-purpose text-to-video without pose control
- you lack a GPU or cannot run diffusion models locally
- you need lip-sync or talking-head generation alone (use MuseTalk instead)
- you require a permissively licensed model for commercial use without checking the custom license

## Facets
- artifact type: framework
- maturity: active
- function: machine-learning, deep-learning, video-processing, image-processing, llm-training
- domain: deep-learning, computer-vision, artificial-intelligence, image-processing
- platform: python
- tags: image-to-video, pose-guided-generation, diffusion-models, virtual-human, dance-generation, animateanyone, video, linux, gpu

## Member repositories
- TMElyralab/MusePose (main) score 28

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:11.426152+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 2026-08-30T02:15:58.132439+00:00, confidence not recorded.
  - readme: https://github.com/TMElyralab/MusePose (fetched 2026-08-28T04:07:11.426152+00:00, sha b5d1b9ee549c)
- Data as of 2026-08-30T08:39:29.467469+00:00.