3D Bird Reconstruction: a Dataset, Model, and Shape Recovery from a Single View (ECCV 2020)

ECCV 2020

3D Bird Reconstruction: a Dataset, Model, and Shape Recovery from a Single View (ECCV 2020)

Aug 18, 2020
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Marc Badger, Yufu Wang, Adarsh Modh, Ammon Perkes, Nikos Kolotouros, Bernd G. Pfrommer, Marc F. Schmidt, and Kostas Daniilidis Abstract: Automated capture of animal pose is transforming how we study neuroscience and social behavior. Movements carry important so- cial 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 dis- played 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 key- point 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.

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