deeplabcut

Markerless animal pose estimation uses deep learning architectures to track body parts and kinematics from video data. It supports single-animal, multi-animal, and 3D pose reconstruction workflows for scientific research.

Yes, zero-shot inference on common species like mice and quadrupeds is possible using pretrained SuperAnimal models. This allows you to automatically quantify gait and social interaction patterns directly from raw video footage.

Markerless behavioral tracking requires DeepLabCut, PyTorch, and associated computer vision dependencies like Torchvision for model inference and training.

Yes, markerless tracking supports single-animal, multi-animal, and 3D pose reconstruction workflows. It uses deep learning to track body parts, keypoints, and kinematics in various behavioral research settings.

The best way to quantify social interaction patterns is using multi-animal markerless pose estimation. It automates behavioral tracking from video data using deep learning to identify keypoints without invasive physical markers.

3D pose reconstruction limitations depend on the pretrained models available for your specific species and the quality of the video data. Inference requires PyTorch and Torchvision dependencies for accurate deep learning model execution.