Animal Pose Labeling Using General-Purpose Point Trackers
This provides a valuable tool for researchers studying animal behavior by enabling more reliable automatic pose labeling, though it is incremental as it builds on existing trackers.
The paper tackles the problem of unreliable animal pose estimation from videos by proposing a test-time optimization pipeline that fine-tunes a pre-trained point tracker on sparse annotations, achieving state-of-the-art performance with reasonable annotation cost.
Automatically estimating animal poses from videos is important for studying animal behaviors. Existing methods do not perform reliably since they are trained on datasets that are not comprehensive enough to capture all necessary animal behaviors. However, it is very challenging to collect such datasets due to the large variations in animal morphology. In this paper, we propose an animal pose labeling pipeline that follows a different strategy, i.e. test time optimization. Given a video, we fine-tune a lightweight appearance embedding inside a pre-trained general-purpose point tracker on a sparse set of annotated frames. These annotations can be obtained from human labelers or off-the-shelf pose detectors. The fine-tuned model is then applied to the rest of the frames for automatic labeling. Our method achieves state-of-the-art performance at a reasonable annotation cost. We believe our pipeline offers a valuable tool for the automatic quantification of animal behavior. Visit our project webpage at https://zhuoyang-pan.github.io/animal-labeling.