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Single-Shot 3D Multi-Person Shape Reconstruction from a Single RGB Image.

Seong Hyun Kim1, Ju Yong Chang1

  • 1Department of Electronics and Communication Engineering, Kwangwoon University, Seoul 01897, Korea.

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|December 8, 2020
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Summary

This study introduces a novel deep learning model for real-time, single-shot 3D multi-person shape reconstruction from a single RGB image. The method accurately reconstructs multiple 3D human shapes in the camera coordinate system.

Keywords:
3D human shape reconstructiondeep neural networkstatistical body shape model

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Area of Science:

  • Computer Vision
  • Machine Learning
  • 3D Computer Graphics

Background:

  • Current 3D human shape reconstruction methods often focus on single individuals.
  • Existing techniques typically require ground-truth depth information for camera coordinate system conversion.
  • Root-relative 3D shape reconstruction is common, lacking absolute positional data.

Purpose of the Study:

  • To develop an end-to-end learning-based model for single-shot, 3D, multi-person shape reconstruction.
  • To achieve reconstruction directly in the camera coordinate system from a single RGB image.
  • To enable real-time processing of 3D multi-person shape reconstruction.

Main Methods:

  • A novel end-to-end deep learning network is proposed for single-shot 3D multi-person shape reconstruction.
  • The network utilizes output tensors divided into grid cells, with each cell containing subject-specific information.
  • The model predicts the absolute root joint position alongside root-relative 3D shape reconstruction.

Main Results:

  • The proposed network enables single-shot 3D reconstruction of multiple persons in the camera coordinate system.
  • The model successfully reconstructs root-relative 3D shapes and predicts absolute root joint positions.
  • The system achieves real-time performance, processing images at approximately 37 frames per second.

Conclusions:

  • The developed model offers a significant advancement in 3D multi-person shape reconstruction from single RGB images.
  • This approach overcomes limitations of previous methods by handling multiple subjects and directly outputting results in the camera coordinate system.
  • The real-time capability makes the method suitable for various practical applications.