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

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 article 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 tech

SUBMITTER: Liu S 

PROVIDER: S-EPMC11935756 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature