  The next generation of Internet services, such as Metaverse, rely on mixed
reality (MR) technology to provide immersive user experiences. However, the
limited computation power of MR headset-mounted devices (HMDs) hinders the
deployment of such services. Therefore, we propose an efficient information
sharing scheme based on full-duplex device-to-device (D2D) semantic
communications to address this issue. Our approach enables users to avoid heavy
and repetitive computational tasks, such as artificial intelligence-generated
content (AIGC) in the view images of all MR users. Specifically, a user can
transmit the generated content and semantic information extracted from their
view image to nearby users, who can then use this information to obtain the
spatial matching of computation results under their view images. We analyze the
performance of full-duplex D2D communications, including the achievable rate
and bit error probability, by using generalized small-scale fading models. To
facilitate semantic information sharing among users, we design a contract
theoretic AI-generated incentive mechanism. The proposed diffusion model
generates the optimal contract design, outperforming two deep reinforcement
learning algorithms, i.e., proximal policy optimization and soft actor-critic
algorithms. Our numerical analysis experiment proves the effectiveness of our
proposed methods. The code for this paper is available at
https://github.com/HongyangDu/SemSharing
