nghorbani/amass resource

Health v2 · maintenance only

Flags: no_license

Adoption not part of the score

1010 stars · 102 forks observed · 2026-08-28

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

AMASS is a large unified database of human motion capture data representing 15 optical marker-based mocap datasets in a common SMPL+H body model parameterization. This repository provides data preparation tools, loaders, and Jupyter notebook tutorials for using AMASS in research, animation, and deep learning training.

Use cases

• load human motion capture data for deep learning
• animate SMPL or SMPL-X body models from mocap data
• prepare train/validation/test splits of 3D human motion
• visualize 3D human body meshes from motion data
• generate synthetic mocap training data
• research human pose estimation and action recognition

When to choose

• you need large-scale unified 3D human motion data for training motion models
• you work with SMPL/SMPL+H/SMPL-X body models in PyTorch
• you need consistent mocap parameterization across multiple source datasets

When to avoid

• you need raw marker-based mocap data rather than SMPL-parameterized poses
• you need a non-Python or non-PyTorch pipeline
• your use case is outside research licensing terms

Facets

dataset · maturity stable

data-science machine-learning data-visualization etl computer-vision machine-learning graphics simulation python cross-platform motion-capture human-pose smpl body-model 3d-human-motion animation dataset-loader

Member repositories

For agents

markdown · JSON · MCP: product_card(name="nghorbani/amass")