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arxiv: 1809.06079 · v1 · pith:A4SYKQXLnew · submitted 2018-09-17 · 💻 cs.CV

An Integral Pose Regression System for the ECCV2018 PoseTrack Challenge

Xiao Sun , Chuankang Li , Stephen Lin This is my paper
classification 💻 cs.CV
keywords posesystemchallengehumaneccv2018estimationintegralposetrack
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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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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

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    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.

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