# akanazawa/hmr

Project page for End-to-end Recovery of Human Shape and Pose

Repository: https://github.com/akanazawa/hmr
Canonical: https://ross.abutalabs.com/products/hmr
Language: Python
License: NOASSERTION
License Family: other
Last push: 2023-07-10T13:41:54+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3180, "days_push": 1150, "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 1666, forks 397 (observed 2026-08-28T04:05:19.534507+00:00)

## What it is
HMR (Human Mesh Recovery) is a TensorFlow implementation of the CVPR 2018 paper 'End-to-end Recovery of Human Shape and Pose', which regresses 3D human body shape and pose (SMPL parameters) directly from a single RGB image. It includes a demo, webcam demo, and training code with pre-trained models.

## Use cases
- estimate 3d human pose from a single image
- recover human body shape from photos
- run 3d human mesh reconstruction on webcam feed
- convert images to smpl body model parameters
- train a human mesh recovery model
- use openpose keypoints to crop people for pose estimation

## When to choose
- you need 3D human shape and pose from a single RGB image
- you want the original reference implementation of the HMR paper for research
- you need SMPL parameter regression with pre-trained TensorFlow models

## When to avoid
- you need a maintained production-ready library (Python 2.7 / TF 1.3 era code)
- you want PyTorch or TF2 support (use community forks instead)
- you need multi-person 3D pose estimation out of the box

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, computer-vision, image-processing
- domain: computer-vision, machine-learning, deep-learning
- platform: python, windows
- tags: human-pose-estimation, human-shape-recovery, smpl, 3d-human-mesh, tensorflow, cvpr-2018, research-code, linux, gpu

## Member repositories
- akanazawa/hmr (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:19.534507+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 2026-08-30T03:42:57.598939+00:00, confidence not recorded.
  - readme: https://github.com/akanazawa/hmr (fetched 2026-08-28T04:05:19.534507+00:00, sha fba278686775)
- Data as of 2026-08-30T08:39:29.467469+00:00.