arXiv:1809.06079
공식 코드 · Papers with Code
An Integral Pose Regression System for the ECCV2018 PoseTrack Challenge
원문 초록
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.
함께 살펴볼 논문
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Continuous Normalizing Flows for Uncertainty-Aware Human Pose Estimation
PoseGRAF: Geometric-Reinforced Adaptive Fusion for Monocular 3D Human Pose Estimation
Learning Pyramid-structured Long-range Dependencies for 3D Human Pose Estimation
PoseBench3D: A Cross-Dataset Analysis Framework for 3D Human Pose Estimation
HDiffTG: A Lightweight Hybrid Diffusion-Transformer-GCN Architecture for 3D Human Pose Estimation
Systematic Comparison of Projection Methods for Monocular 3D Human Pose Estimation on Fisheye Images
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