MotrixLab/SMPLer-X

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1220 stars · 83 forks observed · 2026-08-28

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

Official code for SMPLer-X, a family of foundation models for expressive human pose and shape estimation (EHPS) that unifies body, hand, and face motion capture from images and video. It provides training, testing, inference, visualization, and a HuggingFace demo built on PyTorch and mmpose.

Use cases

• estimate 3d human body hands and face pose from video
• recover smpl-x mesh from monocular images
• fit human body shape and pose from rgb video
• run motion capture without markers using a single camera
• benchmark expressive human pose and shape estimation models
• fine-tune a foundation model for 3d human mesh recovery

When to choose

• you need state-of-the-art SMPL-X body, hand, and face estimation from RGB input
• you want pretrained EHPS foundation models with scaling-law-backed performance
• you need a research codebase for training and evaluating EHPS models

When to avoid

• you need real-time pose estimation on CPU or edge devices
• you only need lightweight 2D keypoints rather than full 3D SMPL-X meshes
• you require a permissively licensed model for commercial use (license is non-standard)

Facets

library · maturity stable

machine-learning deep-learning computer-vision image-processing video-processing computer-vision machine-learning deep-learning artificial-intelligence python human-pose-estimation smpl-x ehps 3d-human-mesh foundation-model neurips-2023 body-hands-face motion-capture gpu docker linux

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