An adversarial feature learning based semantic communication method for Human 3D Reconstruction

TL;DR

This paper presents a novel semantic communication approach using adversarial feature learning for efficient human 3D reconstruction, optimizing data transmission and improving reconstruction quality in bandwidth-limited scenarios.

Contribution

It introduces a multitask feature extraction, semantic encoding, dynamic compression, and a ViT-diffusion based reconstruction, advancing bandwidth-efficient 3D human body modeling.

Findings

Enhanced data transmission efficiency and reduced latency.

Improved 3D reconstruction quality in bandwidth-limited environments.

Effective semantic encoding and decoding techniques demonstrated.

Abstract

With the widespread application of human body 3D reconstruction technology across various fields, the demands for data transmission and processing efficiency continue to rise, particularly in scenarios where network bandwidth is limited and low latency is required. This paper introduces an Adversarial Feature Learning-based Semantic Communication method (AFLSC) for human body 3D reconstruction, which focuses on extracting and transmitting semantic information crucial for the 3D reconstruction task, thereby significantly optimizing data flow and alleviating bandwidth pressure. At the sender's end, we propose a multitask learning-based feature extraction method to capture the spatial layout, keypoints, posture, and depth information from 2D human images, and design a semantic encoding technique based on adversarial feature learning to encode these feature information into semantic data. We also develop a dynamic compression technique to efficiently transmit this semantic data, greatly enhancing transmission efficiency and reducing latency. At the receiver's end, we design an efficient multi-level semantic feature decoding method to convert semantic data back into key image features. Finally, an improved ViT-diffusion model is employed for 3D reconstruction, producing human body 3D mesh models. Experimental results validate the advantages of our method in terms of data transmission efficiency and reconstruction quality, demonstrating its excellent potential for application in bandwidth-limited environments.