A Novel Mesh-Based 3D Mouse Pose Estimation with Low Jitter
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Luo, Zhangqi
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Abstract
Animal keypoint prediction is fundamental for neuroscience research. The existing off-shelf solution focus on the
accuracy of keypoint prediction but doesn’t address the jitter. In this paper, we present a novel pose estimation method
that leverages the 3D animal mesh and skeletons generated by SMAL model. We utilize animal 3D reconstruction results to stabilize the prediction of key points and reduces the jitter. Our approach is eventually experimented on mouse
key points prediction.
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Date
2024-08-20
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