Recent advances in artificial intelligence (AI), coupled with a surge in training data, have led to the widespread use of AI for digital content generation, with ChatGPT serving as a representative example. Despite the increased efficiency and diversity, the inherent instability of AI models poses a persistent challenge in guiding these models to produce the desired content for users. In this paper, we introduce an integration of wireless perception (WP) with AI-generated content (AIGC) and propose a unified WP-AIGC framework to improve the quality of digital content production. The framework employs a novel multi-scale perception technology to read user's posture, which is difficult to describe accurately in words, and transmits it to the AIGC model as skeleton images. Based on these images and user's service requirements, the AIGC model generates corresponding digital content. Since the production process imposes the user's posture as a constraint on the AIGC model, it makes the generated content more aligned with the user's requirements. Additionally, WP-AIGC can also accept user's feedback, allowing adjustment of computing resources at edge server to improve service quality. Experiments results verify the effectiveness of the WP-AIGC framework, highlighting its potential as a novel approach for guiding AI models in the accurate generation of digital content.

翻译：近年来，人工智能（AI）的快速发展，配合大量的训练数据，已经广泛应用于数字内容生成，以ChatGPT为代表。尽管具有更高效和更多样化的特点，但AI模型固有的不稳定性仍然是指导这些模型生成所需内容的持久挑战。在本文中，我们介绍了将无线感知（WP）与AI生成的内容（AIGC）相结合的方法，并提出了一个统一的WP-AIGC框架，以提高数字内容生成的质量。该框架采用了一种新颖的多尺度感知技术，读取用户的姿势并将其作为骨架图像传输给AIGC模型。根据这些图像和用户的服务要求，AIGC模型生成相应的数字内容。由于生成过程将用户的姿势作为AIGC模型的约束，使生成的内容更符合用户的要求。此外，WP-AIGC还可以接受用户的反馈，允许调整边缘服务器上的计算资源以改善服务质量。实验结果验证了WP-AIGC框架的有效性，突显其作为指导AI模型准确生成数字内容的新方法的潜力。