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）的突破性进展与训练数据的激增相结合，使得AI被广泛用于数字内容生成，其中ChatGPT是典型代表。尽管AI技术提升了数字内容生成的效率与多样性，但AI模型固有的不稳定性对引导模型生成用户期望内容构成了持续挑战。本文提出将无线感知（Wireless Perception, WP）与AI生成内容（AI-Generated Content, AIGC）相融合，并设计统一框架WP-AIGC以提升数字内容生成质量。该框架采用新型多尺度感知技术，读取用户难以用语言精确描述的姿态信息，并将其以骨架图像形式传输至AIGC模型。AIGC模型根据这些图像及用户服务需求生成相应数字内容。由于生成过程将用户姿态作为约束条件施加于AIGC模型，使得生成内容更契合用户需求。此外，WP-AIGC还能接收用户反馈，通过调整边缘服务器计算资源来优化服务质量。实验结果验证了WP-AIGC框架的有效性，凸显其作为引导AI模型精准生成数字内容新方法的潜力。