Pose-VAE: A Pose-Guided End-to-End Semantic Communication System With Semantic Knowledge Base

Abstract

While deep learning has considerably advanced semantic communication, image transmission techniques continue to face persistent challenges, particularly in capturing spatial structure and preserving semantic integrity against channel noise. To address these issues, we propose a pose-guided end-to-end semantic communication system with a semantic knowledge base (Pose-VAE). Specifically, we integrate human pose information to guide semantic feature extraction, enabling the network to model spatial structures. In addition, we design a novel semantic knowledge base (SKB) that encodes clustered pose categories as structural priors to enhance robustness against channel noise. Extensive experiments demonstrate that Pose-VAE significantly improves image reconstruction quality, particularly under the low signal-to-noise ratio (SNR) conditions.