3D human pose estimation with adaptive receptive fields and dilated temporal convolutions
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In this work, we demonstrate that receptive fields in 3D pose estimation can be effectively specified using optical flow. We introduce adaptive receptive fields, a simple and effective method to aid receptive field selection in pose estimation models based on optical flow inference. We contrast the performance of a benchmark state-of-the-art model running on fixed receptive fields with their adaptive field counterparts. By using a reduced receptive field, our model can process slow-motion sequences (10x longer) 23 benchmark model running at regular speed. The reduction in computational cost is achieved while producing a pose prediction accuracy to within 0.36 benchmark model.
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