Three-dimensional markerless pose estimation from multi-view video is emerging as an exciting method for quantifying the behavior of freely moving animals. Nevertheless, scientifically precise 3D animal pose estimation remains challenging, primarily due to a lack of large training and benchmark datasets and the immaturity of algorithms tailored to the demands of animal experiments and body plans. Existing techniques employ fully supervised convolutional neural networks (CNNs) trained to predict body keypoints in individual video frames, but this demands a large collection of labeled training samples to achieve desirable 3D tracking performance. Here, we introduce a semi-supervised learning strategy that incorporates unlabeled video frames via a simple temporal constraint applied during training. In freely moving mice, our new approach improves the current state-of-the-art performance of multi-view volumetric 3D pose estimation and further enhances the temporal stability and skeletal consistency of 3D tracking.