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Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

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DE-HRNet: Detail enhanced high-resolution network for human pose estimation.

Yuxuan Liu¹, Guohui Zhou¹, Wei He¹

¹School of Computer Science and Information Engineering, Harbin Normal University, Harbin, China.

|September 2, 2025

Summary

This summary is machine-generated.

Scale variation in human pose estimation is addressed by the Detail Enhanced High-Resolution Network (DE-HRNet). This model improves accuracy for small body joints by enhancing local features and using a dynamic sampler to preserve detail during up-sampling.

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Area of Science:

Computer Vision
Machine Learning

Background:

Scale variation presents a significant challenge in human pose estimation, impacting the accuracy of keypoint prediction, especially for smaller body parts.
Existing methods struggle to maintain accuracy across diverse human body scales.

Purpose of the Study:

To propose a novel network, the Detail Enhanced High-Resolution Network (DE-HRNet), to effectively handle scale variations in human pose estimation.
To improve the accuracy and robustness of keypoint detection across different body part scales.

Main Methods:

Introduction of a Detail Enhancement Module (DEM) to recover and enhance lost low-level features crucial for scale-varying keypoints.
Implementation of an ultra-lightweight dynamic sampler (dySample) to replace nearest up-sampling, minimizing detail loss during resolution enhancement.

Main Results:

DE-HRNet achieved 75.6 AP on the COCO test-dev2017 dataset and 90.7 PCKh@0.5 on the MPII valid dataset.
Performance improvements of 0.7 AP and 0.4 PCKh@0.5 were observed compared to the standard High-Resolution Network (HRNet).
The proposed method demonstrated strong performance in handling scale variations compared to existing approaches.

Conclusions:

DE-HRNet effectively mitigates the impact of scale variations in human pose estimation by preserving local details.
The proposed DEM and dySample contribute to enhanced accuracy and robustness, particularly for challenging scale-varying keypoints.