open-mmlab/mmhuman3d

Health v2 · maintenance only

Adoption not part of the score

1426 stars · 156 forks observed · 2026-08-28

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

MMHuman3D is an open-source PyTorch-based toolbox and benchmark for 3D human parametric models (e.g., SMPL, SMPL-X) in computer vision and graphics, part of the OpenMMLab project. It provides modular reimplementations of state-of-the-art methods, a unified HumanData data convention, and versatile rendering/visualization tools.

Use cases

• reproduce state-of-the-art 3D human pose and mesh recovery methods
• train and evaluate models on human pose datasets with a unified format
• render SMPL/SMPL-X body models with segmentation, depth, and keypoints
• convert and preprocess human motion capture datasets
• run inference demos including webcam real-time estimation

When to choose

• you need a modular PyTorch framework for 3D human parametric model research
• you want to benchmark multiple HMR methods under one codebase
• you need unified handling of many human pose datasets via HumanData
• you need differentiable or conventional rendering of human body models

When to avoid

• you need a production-ready human pose estimation service rather than a research toolbox
• you work outside PyTorch or need multi-view mocap (see XRMoCap instead)
• you need actively developed features, as the project appears to be in maintenance mode

Facets

framework · maturity maintenance

machine-learning deep-learning computer-vision image-processing data-visualization benchmarking computer-vision machine-learning deep-learning graphics simulation python 3d-human-pose smpl parametric-model human-mesh-recovery pytorch openmmlab motion-capture linux gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="open-mmlab/mmhuman3d")