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Updated: Feb 23, 2026

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
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Capturing Complex 3D Human Motions with Kernelized Low-Rank Representation from Monocular RGB Camera.

Xuan Wang1,2,3,4, Fei Wang5,6,7,8, Yanan Chen9,10,11,12

  • 1The Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, No.28 Xianning West Road, Xi'an 710048, China. xwang.cv@gmail.com.

Sensors (Basel, Switzerland)
|September 5, 2017
PubMed
Summary

This study introduces a novel kernelized low-rank representation for Non-Rigid Structure from Motion (NRSfM) to improve 3D structure recovery from image sequences. The method enhances accuracy in reconstructing complex human motions and enables marker-less pose estimation.

Keywords:
3D human pose estimationkernel low-rank representationmonocular reconstructionnon-rigid structure from motion

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Robotics

Background:

  • Recovering 3D structures from monocular image sequences is challenging due to inherent ambiguities.
  • Existing methods often rely on priors like low-rank shape bases to resolve these ambiguities.

Purpose of the Study:

  • To develop a more accurate Non-Rigid Structure from Motion (NRSfM) method using kernelized low-rank representation.
  • To extend the NRSfM method for marker-less 3D human pose estimation.

Main Methods:

  • Proposed a NRSfM method based on the assumption that 3D structures lie on the union of nonlinear subspaces.
  • Utilized a kernelized low-rank representation with a soft-inextensibility constraint for accurate 3D human motion recovery.
  • Integrated a Convolutional Neural Network (CNN) based 2D human joint detector for marker-less pose estimation.

Main Results:

  • The kernelized low-rank representation effectively models complex deformations, leading to more accurate 3D reconstructions.
  • The marker-based NRSfM method demonstrated strong performance on UMPM and CMU MoCap datasets.
  • The marker-less approach showed potential for real-life applications due to CNN integration.

Conclusions:

  • Kernelized low-rank representation offers superior modeling of complex deformations in NRSfM.
  • The proposed marker-less 3D human pose estimation method is robust and applicable to real-world scenarios.