Walter0807/MotionBERT

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1439 stars · 182 forks observed · 2026-08-28

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

Official PyTorch implementation of MotionBERT (ICCV 2023), a unified pretrained model for learning human motion representations from 2D skeletons. It supports 3D human pose estimation, skeleton-based action recognition, and mesh recovery, including in-the-wild inference on custom videos.

Use cases

• estimate 3d human pose from 2d keypoints
• recover 3d human mesh from monocular video
• recognize human actions from skeleton sequences
• extract human-centric video representations from motion data
• run 3d pose estimation on my own custom videos
• pretrain a unified human motion representation model

When to choose

• You need a single pretrained model that handles 3D pose, mesh recovery, and skeleton-based action recognition
• You want a research baseline or reproducible implementation from a published ICCV 2023 paper with checkpoints
• You want to run in-the-wild inference on your own videos with human-centric motion features

When to avoid

• You need a lightweight real-time pose estimator for production or edge deployment with strict latency budgets
• You work outside PyTorch, e.g. need TensorFlow, ONNX, or mobile-native inference pipelines
• You only need 2D pose detection from raw images, since this focuses on lifting 2D skeletons to 3D

Facets

library · maturity active

machine-learning deep-learning computer-vision transformers video-processing computer-vision deep-learning artificial-intelligence machine-learning python pytorch 3d-pose-estimation human-motion mesh-recovery action-recognition skeleton-sequence pretrained-models research-code iccv-2023 motion-representation video gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="Walter0807/MotionBERT")