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

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DUA: A Domain-Unified Approach for Cross-Dataset 3D Human Pose Estimation.

João Renato Ribeiro Manesco¹, Stefano Berretti², Aparecido Nilceu Marana¹

¹Faculty of Sciences, UNESP-São Paulo State University, Bauru 17033-360, SP, Brazil.

Sensors (Basel, Switzerland)

|September 9, 2023

Summary

This study introduces a Domain Unified approach for 3D human pose estimation, significantly reducing errors by 29.24% in cross-dataset scenarios. The method enhances generalization by integrating synthetic data, improving accuracy in real-world applications.

Keywords:

3D human pose estimation adversarial neural networks domain adaptation

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Area of Science:

Computer Vision
Machine Learning
Robotics

Background:

Deep learning excels in 2D human pose estimation.
3D pose estimation offers more robust results but lacks sufficient labeled data.
Current two-step methods using 2D pose inputs struggle with generalization across datasets.

Purpose of the Study:

To address pose misalignment in cross-dataset 3D human pose estimation.
To improve the generalization capability of 3D pose estimation systems.
To leverage synthetic data for enhanced domain adaptation.

Main Methods:

Proposed a novel Domain Unified approach.
Integrated three modules: pose converter, uncertainty estimator, and domain classifier.
Employed domain adaptation techniques using synthetic (SURREAL) and real (Human3.6M) datasets.

Main Results:

Achieved a 44.1mm (29.24%) error reduction compared to a no-adaptation scenario.
Demonstrated state-of-the-art performance in cross-dataset evaluation.
Successfully mitigated pose misalignment issues.

Conclusions:

The Domain Unified approach effectively improves 3D human pose estimation accuracy and robustness.
Domain adaptation using synthetic data is crucial for generalizing pose estimation models.
The proposed method offers a significant advancement for real-world human pose understanding.