ViTAE-Transformer/ViTPose

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

2138 stars · 268 forks observed · 2026-08-28

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

Official PyTorch implementation of ViTPose and ViTPose++, Vision Transformer models for human and generic body pose estimation from NeurIPS 2022 and TPAMI 2023 papers. It provides pretrained checkpoints, training/evaluation configs, and achieves state-of-the-art results such as 81.1 AP on MS COCO keypoints.

Use cases

• estimate human body keypoints from images
• run pose estimation on video
• fine-tune a vision transformer for pose estimation
• benchmark pose estimation models on MS COCO
• use MAE-pretrained backbones for keypoint detection
• distill large pose models into smaller ones

When to choose

• you need high-accuracy 2D human pose estimation in PyTorch
• you want a simple vision transformer baseline for keypoints
• you need pretrained checkpoints for COCO-style pose tasks

When to avoid

• you need 3D pose estimation or full body mesh recovery
• you want a plug-and-play production API rather than research code
• you work outside PyTorch or lack GPU resources

Facets

library · maturity stable

machine-learning deep-learning computer-vision computer-vision deep-learning machine-learning python pose-estimation vision-transformer pytorch human-pose keypoint-detection mae-pretraining knowledge-distillation research-code gpu

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markdown · JSON · MCP: product_card(name="ViTAE-Transformer/ViTPose")