Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Apr 1, 2026

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

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

Published on: April 21, 2023

Multimodal Deep Autoencoder for Human Pose Recovery.

Chaoqun Hong, Jun Yu, Jian Wan

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|October 10, 2015

Summary

This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Holistic Invariant Retracing for Distortion-Resilient Multi-Modal Learning in Spatial Transcriptomics.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same author

Demonstration of efficient predictive surrogates for large-scale quantum processors.

Nature communications·2026

Same author

A DeepSeek-powered AI system for automated chest radiograph interpretation in clinical practice.

Nature communications·2026

Same author

NoisePO: Efficient Semantic Noise Generation and Ranking for Diffusion-Based Text-to-Image Synthesis.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same author

Stability and Generalization for Distributed SGDA.

IEEE transactions on pattern analysis and machine intelligence·2026

Same author

SPAgent: Adaptive Task Decomposition and Model Selection for General Video Generation and Editing.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

Change-Prior-Guided Unsupervised Change Detection of Heterogeneous Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

AgonicDreamer: Enhancing Multi-View Consistency in Text-to-3D Generation via Rectified Score Distillation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

BiCM-Prompt: Bidirectional Cross-Modal Prompt Tuning for Class-Incremental Learning on Multisource Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

GoP-based Quality Enhancement on Video Compression.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

Align then Tensorize: Multi-Level Consistent Anchor Graph Learning for Scalable Multi-View Clustering.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

Beyond Fidelity: Diverse Image Synthesis via Retrieval-Augmented Diffusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

See all related articles

This study introduces a new non-linear deep learning method for human pose recovery from videos. The novel approach significantly reduces recovery errors by accurately mapping 2D images to 3D poses.

Area of Science:

Computer Vision
Machine Learning
Biomedical Engineering

Background:

Traditional human pose recovery methods often assume linear mappings between 2D images and 3D poses.
This linear assumption limits performance due to the inherently non-linear relationship between visual data and human articulation.

Purpose of the Study:

To develop a novel human pose recovery method utilizing non-linear mapping.
To improve the accuracy and performance of 3D human pose estimation from video data.

Main Methods:

Employs a multi-layered deep neural network for non-linear mapping.
Integrates feature extraction with multimodal fusion using hypergraph Laplacian and low-rank representation.
Utilizes back-propagation deep learning with parameter fine-tuning for mapping 2D images to 3D poses.

More Related Videos

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Related Experiment Videos

Last Updated: Apr 1, 2026

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

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

Published on: April 21, 2023

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Main Results:

Achieved a 20%-25% reduction in human pose recovery error across three datasets.
Demonstrated the effectiveness of the proposed non-linear mapping approach.

Conclusions:

The proposed method effectively overcomes the limitations of linear assumptions in human pose recovery.
Deep learning with multimodal fusion offers a promising direction for accurate 3D human pose estimation.