Animal Pose Labeling Using General-Purpose Point Trackers

2025-06-04

AI Summary

Existing animal pose estimation methods suffer from poor generalization and high annotation costs due to insufficient training data coverage and substantial morphological diversity across species. To address this, we propose a lightweight test-time optimization framework for animal behavior analysis: given only 3–5 sparsely annotated frames per video—either manually labeled or generated by a detector—we fine-tune lightweight appearance embeddings of a generic point tracker (e.g., TrackAnything or RAFT) to propagate high-accuracy pose estimates across the entire video. This work is the first to adapt generic point trackers to animal pose annotation, circumventing reliance on large-scale labeled datasets through test-time adaptation. Evaluated on multi-species video benchmarks, our method achieves state-of-the-art performance while reducing annotation cost by over 80%, significantly improving cross-morphology generalization and practical deployment efficiency.

Technology Category

• Computer Vision: Motion & Tracking
• Machine Learning: Multimodal Learning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

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.

Problem

• Automatically estimating animal poses from videos
• Overcoming limitations of existing methods with test-time optimization
• Reducing annotation cost while improving pose labeling accuracy

Innovation

• Uses general-purpose point trackers
• Fine-tunes lightweight appearance embedding
• Applies test-time optimization strategy