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Human Mesh Reconstruction with Generative Adversarial Networks from Single RGB Images.

Rui Gao1, Mingyun Wen1, Jisun Park1

  • 1Department of Multimedia Engineering, Dongguk University-Seoul, 30, Pildongro-1-gil, Jung-gu, Seoul 04620, Korea.

Sensors (Basel, Switzerland)
|March 6, 2021
PubMed
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This study introduces a novel method to create 3D human models from single images, reducing costs for virtual city development. The technique reconstructs complete human meshes and poses, enhancing smart city simulations.

Area of Science:

  • Computer Vision
  • 3D Reconstruction
  • Machine Learning

Background:

  • Smart city development necessitates realistic virtual environments.
  • Creating diverse 3D human models is crucial but costly.
  • Existing methods often require multiple sensors or complex setups.

Purpose of the Study:

  • To propose a cost-effective method for reconstructing 3D human meshes from single RGB images.
  • To reduce reliance on multiple sensors for 3D human data acquisition.
  • To facilitate the creation of virtual cities and human simulations.

Main Methods:

  • Utilizing a generative adversarial network (GAN) with a novel shape-pose-based generator.
  • Employing deep convolutional neural networks for mesh generation.
Keywords:
3D human modelGANartificial intelligencedeep learningimage processingsmart cities

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  • Implementing an enhanced multi-source discriminator for improved accuracy.

    • Main Results:

    • Achieved 92.1% accuracy in body shape recovery.
    • Demonstrated real-time processing at 34 images per second.
    • Significantly outperformed previous state-of-the-art methods.

    Conclusions:

    • The proposed method enables efficient and accurate 3D human mesh and pose reconstruction from single images.
    • This facilitates the generation of diverse 3D human models for virtual city simulations.
    • The approach reduces costs and complexity in 3D human modeling for smart city applications.