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

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Published on: April 21, 2023

Parametric Human Body Reconstruction Based on Sparse Key Points.

Ke-Li Cheng, Ruo-Feng Tong, Min Tang

IEEE Transactions on Visualization and Computer Graphics

|February 4, 2016

Summary

This summary is machine-generated.

This study introduces an efficient algorithm for automatic human body reconstruction using a single Kinect sensor. The method uses sparse key points and regression for fast and accurate 3D body modeling.

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Area of Science:

Computer Vision
3D Reconstruction
Human Body Modeling

Background:

3D human body reconstruction is crucial for applications like virtual reality and robotics.
Traditional methods often require complex setups and extensive computation time.
Efficient and accurate reconstruction from single sensor data remains a challenge.

Purpose of the Study:

To develop an automatic parametric human body reconstruction algorithm.
To enable efficient 3D model construction using a single Kinect sensor.
To reduce the runtime of human body reconstruction while maintaining accuracy.

Main Methods:

Utilizes a single Kinect sensor for range data acquisition.
Employs sparse key point regression for correspondence matching.
Incorporates feature descriptors and regression stages for robustness.
Applies a pre-factorization method for dense refinement and computational efficiency.

Main Results:

Achieves comparable reconstruction accuracy to traditional methods.
Significantly reduces the runtime for 3D human body modeling.
Successfully reconstructs user's body shape and pose in seconds.

Conclusions:

The proposed algorithm offers an efficient and accurate solution for single-sensor human body reconstruction.
Sparse key point regression is an effective alternative to dense correspondence optimization.
The method has potential for real-time applications requiring rapid 3D body modeling.