# HumanAIGC/EMO

Emote Portrait Alive: Generating Expressive Portrait Videos with Audio2Video Diffusion Model under Weak Conditions

Repository: https://github.com/HumanAIGC/EMO
Canonical: https://ross.abutalabs.com/products/emo
License Family: other
Last push: 2024-08-21T09:40:06+00:00

## Health v2 (maintenance only)
Score: 25/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 65
- inputs: {"age_days": 918, "days_push": 742, "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 7594, forks 928 (observed 2026-08-28T04:10:01.872534+00:00)

## What it is
EMO (Emote Portrait Alive) is a research codebase from Alibaba's Institute for Intelligent Computing that generates expressive talking portrait videos from a single reference image and an audio clip, using an audio2video diffusion model under weak conditions. It was published at ECCV 2024 and is primarily a paper companion release.

## Use cases
- generate a talking head video from a photo and audio
- animate a portrait image to match speech audio
- create expressive avatar videos with audio-driven diffusion
- reproduce ECCV 2024 audio2video portrait research
- build lip-synced character videos from voice recordings

## When to choose
- you need state-of-the-art audio-driven portrait video generation for research
- you want to study or extend the EMO diffusion approach
- you have GPU resources and are comfortable with research-grade code

## When to avoid
- you need a production-ready tool with a stable API or license
- you lack a GPU or cannot run heavy diffusion inference
- you need commercial usage rights - the repo has no license
- you want a polished end-user application rather than research code

## Facets
- artifact type: library
- maturity: experimental
- function: machine-learning, deep-learning, video-processing, audio-processing, computer-vision, speech-recognition
- domain: artificial-intelligence, deep-learning, computer-vision
- platform: python
- tags: audio2video, diffusion-model, talking-head, portrait-animation, research-code, eccv-2024, no-license, video, audio, gpu, linux

## Member repositories
- HumanAIGC/EMO (main) score 25

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:01.872534+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 2026-08-29T17:36:52.429150+00:00, confidence not recorded.
  - readme: https://github.com/HumanAIGC/EMO (fetched 2026-08-28T04:10:01.872534+00:00, sha 3e0fe81fca14)
- Data as of 2026-08-30T08:39:29.467469+00:00.