Abstract: To address the core challenges such as the difficulty in obtaining large scale labeled data, high costs, and data scarcity in animal pose estimation, a semi-supervised animal pose estimation method based on pseudo-labels and consistency training(Pcapose)was proposed to enhance the model's performance and generalization ability under limited labeled data conditions. An initial model was trained based on the initial labeled data. By jointly using a large amount of unlabeled data, a triple mechanism was constructed to improve the quality of pseudo-labels and the robustness of the model. A confidence driven pseudo-label screening strategy was adopted to select samples with low loss and high confidence to expand the training set. A multi-view consistency detection mechanism was introduced to integrate perturbation information from geometry, illumination, and feature space for re-evaluating and screening samples with high loss and low confidence. A teacher-student consistency framework was built to ensure the teacher model provided stable and accurate pseudo-labels. The experimental results on the AP-10K and Grévy's Zebra datasets showed that Pcapose significantly outperformed existing semi-supervised methods in terms of key point detection accuracy and model robustness, demonstrating superior performance in data scarce scenarios.

Key words: animal pose estimation, semi-supervised learning, pseudo-label screening, multi-view consistency, teacher-student consistency, coordinate classification, data scarcity