# yfeng95/PRNet

Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network (ECCV 2018)

Repository: https://github.com/yfeng95/PRNet
Canonical: https://ross.abutalabs.com/products/prnet
Homepage: http://openaccess.thecvf.com/content_ECCV_2018/papers/Yao_Feng_Joint_3D_Face_ECCV_2018_paper.pdf
Language: Python
License: MIT
License Family: permissive
Topics: 3d, face, reconstruction, alignment, swap
Last push: 2022-07-25T23:50:26+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": 3088, "days_push": 1500, "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 5013, forks 932 (observed 2026-08-28T04:09:04.676831+00:00)

## What it is
PRNet is a Python/TensorFlow implementation of the ECCV 2018 Position Map Regression Network for joint 3D face reconstruction and dense alignment from a single image. It runs faster than real-time on GPU and supports applications like pose estimation, depth images, texture editing, and face swapping.

## Use cases
- reconstruct a 3D face mesh from a single photo
- perform dense facial landmark alignment including visibility
- estimate 3D head pose from an image
- swap faces between photos
- generate depth maps of faces
- edit facial textures like eyes for selfie editing

## When to choose
- you need end-to-end 3D face reconstruction without 3DMM fitting
- you need dense alignment with over 40K points rather than just 68 landmarks
- you need real-time performance on a GPU
- you want face swapping or texture editing capabilities

## When to avoid
- you need a maintained library with recent updates or modern framework support
- you cannot use TensorFlow 1.x or Python 2.7 era dependencies
- you need multi-face reconstruction in a single image
- you only need lightweight 2D landmark detection

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, computer-vision, image-processing, deep-learning
- domain: computer-vision, image-processing, deep-learning, artificial-intelligence
- platform: python
- tags: 3d-face-reconstruction, face-alignment, face-swapping, position-map-regression, eccv-2018, tensorflow, linux, gpu

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

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:04.676831+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 2026-08-29T18:17:32.545638+00:00, confidence not recorded.
  - readme: https://github.com/yfeng95/PRNet (fetched 2026-08-28T04:09:04.676831+00:00, sha 1110e6cb0b91)
- Data as of 2026-08-30T08:39:29.467469+00:00.