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Obtaining labelled data to train deep learning methods for estimating animalpose is challenging. Recently, synthetic data has been widely used for poseestimation tasks, but most methods still rely on supervised learning paradigmsutilising synthetic images and labels. Can training be fully unsupervised? Is atiny synthetic dataset sufficient? What are the minimum assumptions that wecould make for estimating animal pose? Our proposal addresses these questionsthrough a simple yet effective self-supervised method that only assumes theavailability of unlabelled images and a small set of synthetic 2D poses. Wecompletely remove the need for any 3D or 2D pose annotations (or complex 3Danimal models), and surprisingly our approach can still learn accurate 3D and2D poses simultaneously. We train our method with unlabelled images of horsesmainly collected for YouTube videos and a prior consisting of 2D syntheticposes. The latter is three times smaller than the number of images needed fortraining. We test our method on a challenging set of horse images and evaluatethe predicted 3D and 2D poses. We demonstrate that it is possible to learnaccurate animal poses even with as few assumptions as unlabelled images and asmall set of 2D poses generated from synthetic data. Given the minimumrequirements and the abundance of unlabelled data, our method could be easilydeployed to different animals.

A Horse with no Labels: Self-Supervised Horse Pose Estimation from Unlabelled Images and Synthetic Prior