zju3dv/GVHMR

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1882 stars · 236 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

GVHMR is a research codebase implementing the SIGGRAPH Asia 2024 paper 'World-Grounded Human Motion Recovery via Gravity-View Coordinates'. It recovers world-grounded global 3D human motion (SMPL poses) from monocular video using a gravity-view coordinate representation, with training and inference pipelines.

Use cases

• recover 3d human motion from monocular video
• estimate world-grounded smpl poses from video
• run human pose estimation on a video with a static or moving camera
• reproduce 3dpw rich and emdb benchmark results
• train a human motion recovery model on amass bedlam datasets
• demo human motion capture from a single video

When to choose

• you need global (world-grounded) human motion rather than per-frame camera-relative pose
• you want to reproduce or build on a published state-of-the-art motion recovery method
• you have a GPU and want research-grade training and inference code

When to avoid

• you need a production-ready commercial product (license is non-commercial)
• you need real-time motion capture on CPU-only hardware
• you only need simple 2D pose estimation

Facets

library · maturity active

machine-learning computer-vision deep-learning simulation computer-vision machine-learning artificial-intelligence python human-motion-recovery pose-estimation smpl monocular-video pytorch 3d-human-pose world-grounded-motion research-code gpu linux

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