Quantification of behavior is critical in applications ranging from neuroscience, veterinary medicine and animal conservation efforts. A common key step for behavioral analysis is first extracting relevant keypoints on animals, known as pose estimation. However, reliable inference of poses currently requires domain knowledge and manual labeling effort to build supervised models. We present a series of technical innovations that enable a new method, collectively called SuperAnimal, to develop and deploy deep learning models that require zero additional human labels and model training. SuperAnimal allows video inference on over 45 species with only two global classes of animal pose models. If the models need fine-tuning, we show SuperAnimal models are 10$\times$ more data efficient and outperform prior transfer-learning-based approaches. Moreover, we provide an unsupervised video-adaptation method to refine keypoints in videos. We illustrate the utility of our model in behavioral classification in mice and gait analysis in horses. Collectively, this presents a data-efficient solution for animal pose estimation for downstream behavioral analysis.

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