MBW: Multi-view Bootstrapping in the Wild. (arXiv:2210.01721v1 [cs.CV])

Labeling articulated objects in unconstrained settings have a wide variety of
applications including entertainment, neuroscience, psychology, ethology, and
many fields of medicine. Large offline labeled datasets do not exist for all
but the most common articulated object categories (e.g., humans). Hand labeling
these landmarks within a video sequence is a laborious task. Learned landmark
detectors can help, but can be error-prone when trained from only a few
examples. Multi-camera systems that train fine-grained detectors have shown
significant promise in detecting …

arxiv bootstrapping