Contemporary pose estimation methods enable precise measurements of behavior via
supervised deep learning with hand-labeled video frames. Although effective in many cases …

Deep learning-based computer vision methods are transforming animal behavioural
research. Transfer learning has enabled work in non-model species, but still requires hand …

We introduce a new method for learning a generative model of articulated 3D animal
motions from raw, unlabeled online videos. Unlike existing approaches for 3D motion …

The body of an animal influences how the nervous system produces behavior. Therefore,
detailed modeling of the neural control of sensorimotor behavior requires a detailed model …

Foraging for food is a rich and ubiquitous animal behavior that involves complex cognitive
decisions, and interactions between different individuals and species. There has been …

We introduce UPose3D, a novel approach for multi-view 3D human pose estimation,
addressing challenges in accuracy and scalability. Our method advances existing pose …

Automatic estimation of 3D human pose from monocular RGB images is a challenging and
unsolved problem in computer vision. In a supervised manner, approaches heavily rely on …

Studying the detailed biomechanics of flying animals requires accurate three‐dimensional
coordinates for key anatomical landmarks. Traditionally, this relies on manually digitizing …

Analyzing and training 3D body posture models depend heavily on the availability of joint
labels that are commonly acquired through laborious manual annotation of body joints or via …

Quantification of behavior is critical in diverse applications from neuroscience, veterinary
medicine to animal conservation. A common key step for behavioral analysis is first …