# ShenhanQian/GaussianAvatars

[CVPR 2024 Highlight] The official repo for "GaussianAvatars: Photorealistic Head Avatars with Rigged 3D Gaussians"

Repository: https://github.com/ShenhanQian/GaussianAvatars
Canonical: https://ross.abutalabs.com/products/gaussianavatars
Homepage: https://shenhanqian.github.io/gaussian-avatars
Language: Python
License: NOASSERTION
License Family: other
Last push: 2026-02-11T10:28:19+00:00

## Health v2 (maintenance only)
Score: 56/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 67, release rhythm 35, longevity 70
- inputs: {"age_days": 980, "days_push": 203, "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 1050, forks 145 (observed 2026-08-28T04:03:22.786945+00:00)

## What it is
Official research code for GaussianAvatars, a CVPR 2024 Highlight method that creates photorealistic, fully controllable head avatars by rigging 3D Gaussian splats to a parametric morphable face model (FLAME). It supports training from multi-view video, expression/pose reenactment from a driving video, and local rendering via a viewer script.

## Use cases
- create photorealistic 3D head avatars from video
- animate a head avatar with expressions from a driving video
- render novel views of a rigged 3D Gaussian avatar
- do research on 3D Gaussian splatting for faces
- reconstruct a controllable FLAME-based avatar from multi-view captures

## When to choose
- you need research-grade photorealistic head avatar reconstruction and reenactment
- you want a rigged, controllable 3D Gaussian representation tied to a morphable face model
- you have multi-view video data and a GPU to train on

## When to avoid
- you need a production or commercial product - the license is CC-BY-NC-SA and forbids commercial use
- you want a turnkey consumer app rather than a Python research pipeline
- you lack multi-view capture data or GPU resources for training

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, computer-vision, graphics, image-processing, simulation
- domain: computer-vision, graphics, artificial-intelligence, deep-learning
- platform: python
- tags: gaussian-splatting, head-avatars, 3d-reconstruction, face-reenactment, research-code, cvpr-2024, neural-rendering, flame, linux, gpu, cuda

## Member repositories
- ShenhanQian/GaussianAvatars (main) score 56

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:22.786945+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 2026-08-30T07:00:16.006988+00:00, confidence not recorded.
  - readme: https://github.com/ShenhanQian/GaussianAvatars (fetched 2026-08-28T04:03:22.786945+00:00, sha 9e5d167ecc74)
  - homepage: https://shenhanqian.github.io/gaussian-avatars (fetched 2026-08-29T13:01:46.753878+00:00, sha 9c63498bf99e)
- Data as of 2026-08-30T08:39:29.467469+00:00.