  Automated capture of animal pose is transforming how we study neuroscience
and social behavior. Movements carry important social cues, but current methods
are not able to robustly estimate pose and shape of animals, particularly for
social animals such as birds, which are often occluded by each other and
objects in the environment. To address this problem, we first introduce a model
and multi-view optimization approach, which we use to capture the unique shape
and pose space displayed by live birds. We then introduce a pipeline and
experiments for keypoint, mask, pose, and shape regression that recovers
accurate avian postures from single views. Finally, we provide extensive
multi-view keypoint and mask annotations collected from a group of 15 social
birds housed together in an outdoor aviary. The project website with videos,
results, code, mesh model, and the Penn Aviary Dataset can be found at
https://marcbadger.github.io/avian-mesh.
