facebookresearch/VideoPose3D

Health v2 · maintenance only

Flags: no_releases archived no_license

Adoption not part of the score

4052 stars · 785 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A PyTorch implementation of CVPR 2019 research on 3D human pose estimation in video using temporal convolutions over 2D keypoint trajectories. It includes pretrained models for Human3.6M and HumanEva-I, training scripts, and visualization tools.

Use cases

• estimate 3D human pose from video
• lift 2D keypoints to 3D poses
• reproduce CVPR 2019 pose estimation baselines
• train a temporal convolution model on Human3.6M
• visualize 3D skeleton predictions from video
• run semi-supervised 3D pose training

When to choose

• you need research-grade 3D human pose estimation from 2D detections
• you want pretrained models benchmarked on Human3.6M
• you are building on temporal convolution approaches to pose lifting

When to avoid

• you need real-time production pose estimation with active support
• you want a maintained library with recent updates
• you need pose estimation for multi-person crowded scenes out of the box

Facets

library · maturity maintenance

computer-vision machine-learning deep-learning computer-vision machine-learning deep-learning python pose-estimation 3d-pose temporal-convolutions pytorch research-code human3.6m gpu

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markdown · JSON · MCP: product_card(name="facebookresearch/VideoPose3D")