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FPCR-Net: Front Point Cloud Regression Network for End-to-End SMPL Parameter Estimation.

Xihang Li1, Xianguo Cheng1,2, Fang Chen1

  • 1College of Mechanical and Automotive Engineering, Ningbo University of Technology, Ningbo 315336, China.

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Summary
This summary is machine-generated.

This study introduces the Front Point Cloud Parametric Body Regression Network (FPCR-Net) for direct human body reconstruction from front point clouds. FPCR-Net significantly reduces errors in vertex and joint positions compared to existing methods.

Keywords:
front body scanparameter regressionparametric body modelingsupervised learning

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Area of Science:

  • Computer Vision
  • 3D Human Pose and Shape Estimation

Background:

  • Obtaining complete 3D human body scans is challenging.
  • Registering parametric body models is time-consuming.

Purpose of the Study:

  • To develop an efficient end-to-end network for human body reconstruction from single front point clouds.
  • To directly regress pose and shape parameters of a parametric body model.

Main Methods:

  • Proposed Front Point Cloud Parametric Body Regression Network (FPCR-Net).
  • Predicts body part labels and back point cloud from front point cloud.
  • Extracts equivariant features and utilizes self-attention for pose and shape prediction.

Main Results:

  • Achieves comparable accuracy to implicit representation methods for body reconstruction.
  • Reduces vertex and joint position errors by 43.2% and 45.0% respectively, compared to baseline regression methods.

Conclusions:

  • FPCR-Net offers an efficient and accurate solution for 3D human body shape and pose estimation from limited input data.
  • The method advances regression-based approaches in human body modeling.