Secure transmission of private information over public channels without arousing suspicion remains a fundamental challenge in stega-nography. Traditional methods modify pixel-level or frequency-domain features, making them vulnerable to detection and degradation. Recent synthesis-based approaches leverage generative models to embed data but often suffer from limited capacity or visual artifacts. In this work, we propose a pose conditioned generative steganographic framework that decouples message representation from image content. Binary messages are first mapped to human poses using a geometry-aware codebook derived from real-world data. These poses then serve as structural conditions to guide diffusion-based image generation, producing semantically coherent and visually natural stego images. By encoding multiple human poses in a single image, our framework increases message capacity while preserving visual coherence. To enhance robustness, we introduce a randomized linear expansion scheme to stabilize pose-code mapping under occlusion and detection noise. We evaluate the method under various perturbations and assess detectability using state-of-the-art steganalysis models. Experimental results show strong imperceptibility, decoding accuracy, and semantic flexibility, highlighting the effectiveness of our framework in enabling secure and coverless generative steganography. The code for PCGS will be released prior to publication.