We propose a method for learning the posture and structure of agents from unlabelled behavioral videos. Starting from the observation that behaving agents are generally the main sources of movement in behavioral videos, our method, Behavioral Keypoint Discovery (B-KinD), uses an encoder-decoder architecture with a geometric bottleneck to reconstruct the spatiotemporal difference between video frames. By focusing only on regions of movement, our approach works directly on input videos without requiring manual annotations. Experiments on a variety of agent types (mouse, fly, human, jellyfish, and trees) demonstrate the generality of our approach and reveal that our discovered keypoints represent semantically meaningful body parts, which achieve state-of-the-art performance on keypoint regression among self-supervised methods. Additionally, B-KinD achieve comparable performance to supervised keypoints on downstream tasks, such as behavior classification, suggesting that our method can dramatically reduce model training costs vis-a-vis supervised methods.
@article{arxiv.2112.05121,
title = {Self-Supervised Keypoint Discovery in Behavioral Videos},
author = {Jennifer J. Sun and Serim Ryou and Roni Goldshmid and Brandon Weissbourd and John Dabiri and David J. Anderson and Ann Kennedy and Yisong Yue and Pietro Perona},
journal= {arXiv preprint arXiv:2112.05121},
year = {2022}
}