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• W4386893488 abstract "Numerous fields, such as ecology, biology, and neuroscience, use animal recordings to track and measure animal behaviour. Over time, a significant volume of such data has been produced, but some computer vision techniques cannot explore it due to the lack of annotations. To address this, we propose an approach for estimating 2D mouse body pose from unlabelled images using a synthetically generated empirical pose prior. Our proposal is based on a recent self-supervised method for estimating 2D human pose that uses single images and a set of unpaired typical 2D poses within a GAN framework. We adapt this method to the limb structure of the mouse and generate the empirical prior of 2D poses from a synthetic 3D mouse model, thereby avoiding manual annotation. In experiments on a new mouse video dataset, we evaluate the performance of the approach by comparing pose predictions to a manually obtained ground truth. We also compare predictions with those from a supervised state-of-the-art method for animal pose estimation. The latter evaluation indicates promising results despite the lack of paired training data. Finally, qualitative results using a dataset of horse images show the potential of the setting to adapt to other animal species." @default.
• W4386893488 created "2023-09-21" @default.
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• W4386893488 date "2023-01-01" @default.
• W4386893488 modified "2023-09-27" @default.
• W4386893488 title "Of Mice and Pose: 2D Mouse Pose Estimation from Unlabelled Data and Synthetic Prior" @default.
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• W4386893488 doi "https://doi.org/10.1007/978-3-031-44137-0_11" @default.
• W4386893488 hasPublicationYear "2023" @default.
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