This paper explores the integration of deep generative models into semantic communication frameworks, highlighting their potential to revolutionize communication tasks and applications. While deep generative models have shown significant advancements in computer vision and natural language processing, their application in communication systems is still underutilized. The authors argue that these models can significantly enhance semantic communication by enabling the regeneration of content that is semantically consistent with the transmitted message, rather than just recovering the original bit sequence. This shift opens new avenues for reducing data traffic and supports novel tasks such as multi-user communication, personalized communication, and content creation. The paper discusses various generative models, including Variational Autoencoders (VAEs), Flow-based models, Generative Adversarial Networks (GANs), and Denoising Diffusion Probabilistic Models (DDPMs), each with its own advantages and limitations. It emphasizes the importance of semantic conditioning in guiding the generation process, ensuring that the regenerated content aligns with the transmitted message. The authors also explore how generative models can improve conventional communication tasks, such as semantic compression and modular architecture, and introduce emerging applications like semantic decomposition for channel-adaptive communication, multimodal semantic diversity for reliable communication, and personalized communication. They highlight the potential of generative models in creating new content based on semantic information, enhancing multi-user communication, and enabling network digital twins. Finally, the paper addresses the challenges and future perspectives of using generative models in communication systems, focusing on computational efficiency, reliability, and explainability. It suggests solutions to these challenges, such as low-bit quantization and adversarial robustness, to ensure the sustainable and trustworthy deployment of generative models in communication frameworks.This paper explores the integration of deep generative models into semantic communication frameworks, highlighting their potential to revolutionize communication tasks and applications. While deep generative models have shown significant advancements in computer vision and natural language processing, their application in communication systems is still underutilized. The authors argue that these models can significantly enhance semantic communication by enabling the regeneration of content that is semantically consistent with the transmitted message, rather than just recovering the original bit sequence. This shift opens new avenues for reducing data traffic and supports novel tasks such as multi-user communication, personalized communication, and content creation. The paper discusses various generative models, including Variational Autoencoders (VAEs), Flow-based models, Generative Adversarial Networks (GANs), and Denoising Diffusion Probabilistic Models (DDPMs), each with its own advantages and limitations. It emphasizes the importance of semantic conditioning in guiding the generation process, ensuring that the regenerated content aligns with the transmitted message. The authors also explore how generative models can improve conventional communication tasks, such as semantic compression and modular architecture, and introduce emerging applications like semantic decomposition for channel-adaptive communication, multimodal semantic diversity for reliable communication, and personalized communication. They highlight the potential of generative models in creating new content based on semantic information, enhancing multi-user communication, and enabling network digital twins. Finally, the paper addresses the challenges and future perspectives of using generative models in communication systems, focusing on computational efficiency, reliability, and explainability. It suggests solutions to these challenges, such as low-bit quantization and adversarial robustness, to ensure the sustainable and trustworthy deployment of generative models in communication frameworks.