# yfeng95/DECA

DECA: Detailed Expression Capture and Animation (SIGGRAPH 2021)

Repository: https://github.com/yfeng95/DECA
Canonical: https://ross.abutalabs.com/products/deca
Language: Python
License: NOASSERTION
License Family: other
Topics: 3d, face, reconstruction, alignment, depth, model, flame
Last push: 2023-07-23T11:15:26+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2339, "days_push": 1137, "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 2514, forks 497 (observed 2026-08-28T04:06:57.612032+00:00)

## What it is
DECA is the official PyTorch implementation of a SIGGRAPH 2021 method that reconstructs a detailed 3D head model (pose, shape, facial detail, lighting) from a single input image using the FLAME face model. The reconstructed model can be animated with realistic wrinkle deformations across poses and expressions.

## Use cases
- reconstruct a 3D face model from a single photo
- animate a face with expressions and poses from an image
- extract detailed facial geometry and wrinkles from photos
- build 3D avatars from unconstrained face images
- benchmark 3D face shape reconstruction accuracy
- export reconstructed head models as obj files
- estimate head pose and lighting from a face image

## When to choose
- you need single-image 3D face reconstruction with fine detail
- you want animatable FLAME-based head models
- you need robust reconstruction under varied poses, lighting, and occlusions
- you are doing research on 3D face modeling or avatar creation

## When to avoid
- you need a production-ready product rather than research code
- you cannot obtain the FLAME model license and trained weights
- you need full-body or multi-person reconstruction
- you require real-time performance on low-end hardware

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, computer-vision, image-processing, deep-learning
- domain: computer-vision, graphics, artificial-intelligence, machine-learning
- platform: python, windows
- tags: 3d-face-reconstruction, flame-model, face-animation, pytorch, single-image, siggraph-2021, 3d-head-modeling, research-code, gpu, linux, macos

## Member repositories
- yfeng95/DECA (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:57.612032+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 2026-08-30T02:26:14.377299+00:00, confidence not recorded.
  - readme: https://github.com/yfeng95/DECA (fetched 2026-08-28T04:06:57.612032+00:00, sha b1a481c56d8b)
- Data as of 2026-08-30T08:39:29.467469+00:00.