# yohanshin/WHAM

Repository: https://github.com/yohanshin/WHAM
Canonical: https://ross.abutalabs.com/products/wham
Language: Python
License: MIT
License Family: permissive
Last push: 2024-04-18T19:21:17+00:00

## Health v2 (maintenance only)
Score: 26/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 71
- inputs: {"age_days": 999, "days_push": 867, "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 1130, forks 141 (observed 2026-08-28T04:03:42.244766+00:00)

## What it is
WHAM is the official PyTorch implementation of the CVPR 2024 paper 'Reconstructing World-grounded Humans with Accurate 3D Motion'. It estimates 3D human pose and motion from monocular video, recovering world-grounded body motion using SMPL body models with optional SLAM and camera calibration.

## Use cases
- reconstruct 3d human motion from a single video
- estimate world-grounded 3d human pose from monocular video
- run human motion capture on smartphone videos
- get smpl body model parameters from video
- estimate camera-coordinate human motion without slam
- refine 3d human pose estimates with temporal smplify

## When to choose
- you need state-of-the-art world-grounded 3D human pose and motion from monocular video
- you want SMPL body model outputs for downstream animation or analysis
- you have videos with known or estimable camera intrinsics and want global motion via SLAM

## When to avoid
- you need real-time pose estimation on edge devices
- you cannot register to download the SMPL/SMPLify body models
- you need multi-person reconstruction in crowded scenes out of the box

## Facets
- artifact type: library
- maturity: active
- function: computer-vision, machine-learning, video-processing
- domain: computer-vision, machine-learning, artificial-intelligence
- platform: python
- tags: 3d-human-pose-estimation, smpl, human-motion-capture, pytorch, research-code, slam, video, linux, macos

## Member repositories
- yohanshin/WHAM (main) score 26

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:42.244766+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 2026-08-30T06:38:17.556671+00:00, confidence not recorded.
  - readme: https://github.com/yohanshin/WHAM (fetched 2026-08-28T04:03:42.244766+00:00, sha fc04967a8bdd)
- Data as of 2026-08-30T08:39:29.467469+00:00.