TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK
3D Human Pose Estimation	Human3.6M	Integral Regression	Average MPJPE (mm)	49.6	# 159
3D Human Pose Estimation	Human3.6M	Integral Regression	PA-MPJPE	40.6	# 59
Pose Estimation	MPII Human Pose	Integral Regression	PCKh-0.5	91.0	# 23

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	`[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/integral-human-pose-regression/pose-estimation-on-mpii-human-pose)](https://paperswithcode.com/sota/pose-estimation-on-mpii-human-pose?p=integral-human-pose-regression)`
	`[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/integral-human-pose-regression/3d-human-pose-estimation-on-human36m)](https://paperswithcode.com/sota/3d-human-pose-estimation-on-human36m?p=integral-human-pose-regression)`

Integral Human Pose Regression

ECCV 2018 · Xiao Sun, Bin Xiao, Fangyin Wei, Shuang Liang, Yichen Wei ·

State-of-the-art human pose estimation methods are based on heat map representation. In spite of the good performance, the representation has a few issues in nature, such as not differentiable and quantization error. This work shows that a simple integral operation relates and unifies the heat map representation and joint regression, thus avoiding the above issues. It is differentiable, efficient, and compatible with any heat map based methods. Its effectiveness is convincingly validated via comprehensive ablation experiments under various settings, specifically on 3D pose estimation, for the first time.

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3D Human Pose Estimation

3D Pose Estimation

Pose Estimation

Quantization

regression

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Human3.6M

MPII

MPII Human Pose

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Ranked #23 on Pose Estimation on MPII Human Pose

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Task	Dataset	Model	Metric Name	Metric Value	Global Rank	Benchmark
3D Human Pose Estimation	Human3.6M	Integral Regression	Average MPJPE (mm)	49.6	# 159	Compare
3D Human Pose Estimation	Human3.6M	Integral Regression	PA-MPJPE	40.6	# 59	Compare
Pose Estimation	MPII Human Pose	Integral Regression	PCKh-0.5	91.0	# 23	Compare

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