Vision-based methods have gained popularity as a tool for helping to analyze the behavior of bats. Though, for bats in the wild, there are still no tools capable of estimating and subsequently analyzing articulated 3D bat pose. We propose a model-based multi-view articulated 3D bat pose estimation framework for this novel problem. Key challenges include the large search space associated with articulated 3D pose, the ambiguities that arise from 2D projections of 3D bodies, and the low resolution image data we have available. Our method uses multi-view camera geometry and temporal constraints to reduce the state space of possible articulated 3D bat poses and finds an optimal set using a Markov Random Field based model.Our experiments use real video data of flying bats and gold-standard annotations by a bat biologist. Our results show, for the first time in the literature, articulated 3D pose estimates being generated automatically for video sequences of bats flying in the wild. The average differences in body orientation and wing joint angles, between estimates produced by our method and those based on gold-standard annotations, ranged from 16• -21 • (i.e., ≈ 17% -23%) for orientation and 14 • -26 • (i.e., ≈ 7% -14%) for wing joint angles.

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Our work introduces a novel way to increase pose estimation accuracy by discovering parts from unannotated regions of training images. Discovered parts are used to generate more accurate appearance likelihoods for traditional part-based models like Pictorial Structures [13] and its derivatives. Our experiments on images of a hawkmoth in flight show that our proposed approach significantly improves over existing work [27] for this application, while also being more generally applicable. Our proposed approach localizes landmarks at least twice as accurately as a baseline based on a Mixture of Pictorial Structures (MPS) model. Our unique High-Resolution Moth Flight (HRMF) dataset is made publicly available with annotations.