Current pose tracking algorithms are evaluated with metrics that do not match the precision needs of movement science for variables such as 3D position, velocity, acceleration, and forces.
An Integral Pose Regression System for the ECCV2018 PoseTrack Challenge
2 Pith papers cite this work. Polarity classification is still indexing.
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abstract
For the ECCV 2018 PoseTrack Challenge, we present a 3D human pose estimation system based mainly on the integral human pose regression method. We show a comprehensive ablation study to examine the key performance factors of the proposed system. Our system obtains 47mm MPJPE on the CHALL_H80K test dataset, placing second in the ECCV2018 3D human pose estimation challenge. Code will be released to facilitate future work.
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UNVERDICTED 2representative citing papers
2D pre-training for 3D human pose estimation yields lower error and higher efficiency than 3D-only training, reaching MPJPE below 64.5 mm on standard benchmarks.
citing papers explorer
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Movement science needs different pose tracking algorithms
Current pose tracking algorithms are evaluated with metrics that do not match the precision needs of movement science for variables such as 3D position, velocity, acceleration, and forces.
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2D Pre-Training for 3D Pose Estimation
2D pre-training for 3D human pose estimation yields lower error and higher efficiency than 3D-only training, reaching MPJPE below 64.5 mm on standard benchmarks.