Traditional image steganography achieves covert information transmission by embedding secret information into cover images, playing a crucial role in information security and data communication. However, the information embedding process inevitably alters the cover image, making it more susceptible to detection by steganalysis tools. In contrast, generative image steganography directly synthesizes stego images from secret information using generative models, avoiding modifications to cover images. Nevertheless, existing generative image steganography methods generally suffer from insufficient robustness against various attacks, particularly geometric attacks, often resulting in damage to the hidden information and failure to effectively extract it. To address this issue, this study proposes a generative steganography method guided by human posture (GSHP). The core idea is to map secret information into human posture features, which are then fed into a generative model to produce stego images. During the information extraction stage, a human posture detection algorithm is used to identify the posture in the stego image, thus recovering the hidden information. Owing to the inherent structural stability of human posture, GSHP exhibits good robustness against various attacks. Extensive experiments also fully demonstrate the advantages of GSHP in terms of security and robustness.