# facebookresearch/DensePose

A real-time approach for mapping all human pixels of 2D RGB images to a 3D surface-based model of the body

Repository: https://github.com/facebookresearch/DensePose
Canonical: https://ross.abutalabs.com/products/densepose
Homepage: http://densepose.org
Language: Jupyter Notebook
License: NOASSERTION
License Family: other
Archived: true
Last push: 2023-01-18T17:26:59+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3012, "days_push": 1323, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, archived, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 7259, forks 1327 (observed 2026-08-28T04:09:57.876944+00:00)

## What it is
DensePose is a research library from Facebook AI that maps all human pixels in 2D RGB images to a 3D surface-based model of the human body in real time. It includes DensePose-RCNN (built on Detectron/Caffe2), the DensePose-COCO dataset, and notebooks for visualization and texture transfer.

## Use cases
- estimate dense human pose from rgb images
- map 2d human pixels to 3d body surface
- train a densepose-rcnn model on coco
- visualize densepose-coco annotations on the smpl model
- transfer textures onto a 3d human body model
- run real-time human body surface estimation

## When to choose
- you need the original DensePose-RCNN implementation or the DensePose-COCO dataset for research
- you want to reproduce results from the DensePose paper
- you need image-to-SMPL surface correspondence annotations

## When to avoid
- you want maintained code or new architectures - use the DensePose project inside Detectron2 instead
- you need production pose estimation without deep learning infrastructure
- you cannot set up Caffe2/Detectron, which are also deprecated

## Facets
- artifact type: library
- maturity: abandoned
- function: computer-vision, machine-learning, deep-learning, image-processing
- domain: computer-vision, machine-learning, artificial-intelligence
- platform: python
- tags: pose-estimation, human-body-modeling, detectron, caffe2, research-code, dataset, smpl, linux, gpu

## Member repositories
- facebookresearch/DensePose (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:57.876944+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 2026-08-29T17:39:42.787702+00:00, confidence not recorded.
  - readme: https://github.com/facebookresearch/DensePose (fetched 2026-08-28T04:09:57.876944+00:00, sha 6411a3bc04cf)
  - homepage: http://densepose.org (fetched 2026-08-29T08:34:22.792559+00:00, sha 2f876c80aa3b)
- Data as of 2026-08-30T08:39:29.467469+00:00.