Lightning Pose Homepage

_images/LightningPose_horizontal_light.png

An end-to-end toolkit for robust multi-view animal pose estimation.

🛰️ Multi-View

Multiview transformers and patch masking for robust 3D tracking.

🎬 Single-View

Temporal context networks that learn from unlabeled video.

☁️ Cloud Ready

Browser-based labeling and training on headless GPU servers.

Multi-view Capabilities

  • Multi-View Transformer (MVT): A unified architecture that enables simultaneous processing of information across all camera views through early-feature fusion.

  • Patch Masking: A novel training scheme that masks random image patches to force the model to learn robust cross-view correspondences.

  • Geometric Consistency: For calibrated setups, the framework incorporates 3D triangulation losses and geometrically-aware 3D data augmentation.

  • Variance Inflation: An advanced technique for outlier detection that identifies geometrically inconsistent predictions.

Single-view Capabilities

  • Temporal Context Networks: Utilizes information from surrounding frames to resolve anatomical ambiguities and maintain tracking through brief occlusions.

  • Unsupervised Learning: Employs training objectives that penalize predictions for violating physical laws.

  • Pretrained Backbone Support: Optimized to work with generic, off-the-shelf Vision Transformer (ViT) backbones.

Cloud Application & Workflow

  • Cloud & Headless Compatibility: A browser-based interface designed for local or cloud deployment.

  • Multi-view Labeling: A specialized annotation tool that streamlines the labeling process by using camera calibration.

  • Unified Multi-view Viewer: Integrated visualization tools to inspect and compare predictions across all camera views simultaneously.

Read the papers

The original Nature Methods 2024 paper that introduced Lightning Pose for single-view pose estimation using semisupervised learning and ensemble kalman smoothing (EKS):

Lightning Pose: improved animal pose estimation via semi-supervised learning, Bayesian ensembling and cloud-native open-source tools
Biderman, D., Whiteway, M. R., Hurwitz, C., et al.

Nature Methods 21, 1316–1328 (2024)

The 2025 paper that added robust multiview support using multiview transformers (MVT), patch masking, 3d image augmentation and losses, and multiview EKS.

An Uncertainty-Aware Framework for Data-Efficient Multi-View Animal Pose Estimation
Aharon, L., Lee, K., et al.

arXiv Preprint (2025)

Get started with the app

The lightning pose app provides an easy-to-use GUI to access most lightning pose features.

To get started, install lightning pose and follow the Create your first project tutorial. It covers the end-to-end workflow of labeling, training, and evaluation.