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Adversarial feature learning for semantic communication in human 3D reconstruction.

Shaojiang Liu1, Jiajun Zou1, Zhendan Liu1

  • 1Guangzhou Xinhua University, Dongguan, China.

Peerj. Computer Science
|March 26, 2025
PubMed
Summary

This study introduces an Adversarial Feature Learning-based Semantic Communication (AFLSC) method for efficient human body 3D reconstruction. AFLSC optimizes data transmission in low-bandwidth settings, enhancing reconstruction quality and reducing latency.

Keywords:
Adversarial feature learningDynamic compressionHuman 3D reconstructionMultilevel semantic feature decodingSemantic communication

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Area of Science:

  • Computer Vision
  • Signal Processing
  • 3D Reconstruction

Background:

  • Human body 3D reconstruction is crucial across many fields.
  • Limited network bandwidth and low latency requirements pose challenges for data transmission and processing efficiency.

Purpose of the Study:

  • To introduce an Adversarial Feature Learning-based Semantic Communication (AFLSC) method for human body 3D reconstruction.
  • To optimize data flow and alleviate bandwidth pressure in limited network environments.

Main Methods:

  • Feature extraction using multitask learning (spatial layout, keypoints, posture, depth).
  • Semantic encoding via adversarial feature learning.
  • Dynamic compression for efficient semantic data transmission.
  • Multi-level semantic feature decoding at the receiver.
  • 3D reconstruction using an improved ViT-diffusion model.

Main Results:

  • Significant optimization of data flow and alleviation of bandwidth pressure.
  • Enhanced transmission efficiency and reduced latency.
  • High-quality human body 3D mesh models generated.
  • Validation of advantages in data transmission efficiency and reconstruction quality.

Conclusions:

  • The proposed AFLSC method demonstrates excellent potential for human body 3D reconstruction in bandwidth-limited environments.
  • The method effectively balances transmission efficiency and reconstruction quality.