Deep generative models are showing great potential in semantic communication, where the receiver regenerates content semantically consistent with the transmitted message rather than recovering the original bits. This paradigm shift offers new opportunities for reducing data traffic and enabling novel applications. The paper presents a unified perspective of deep generative models in semantic communication and their transformative role in future communication frameworks. It discusses the challenges and opportunities in developing generative models tailored for communication systems. Semantic communication involves three levels: effectiveness, semantic, and technical. The semantic level focuses on regenerating content that is semantically equivalent to the transmitted message. Generative models, such as Variational Autoencoders (VAEs), Flow-based Models, Generative Adversarial Networks (GANs), and Diffusion Models, are explored for their capabilities in semantic communication. These models can provide lossy or lossless compression, stable training, and fast sampling, making them suitable for communication tasks. Semantic conditioning is crucial for generative semantic communication, as it guides the generation process. Proper semantic extraction ensures that the receiver can regenerate content consistent with the transmitted message. However, extracting accurate semantic representations remains challenging, especially for multimodal data. The paper also discusses the evolution of communication tasks, including semantic compression beyond perceptual compression and modular architectures beyond end-to-end designs. Generative models can improve communication quality by denoising, restoring, and compressing data, even in the presence of channel errors. Emerging applications of generative models in semantic communication include semantic decomposition for channel-adaptive communication, multimodal semantic diversity for reliable communication, content creation, multi-user communications, network digital twins, multimodal generation, and personalized communication. These applications leverage the flexibility and adaptability of generative models to enhance communication systems. Challenges in deep generative models for semantic communication include computational efficiency, trustworthiness, and explainability. Solutions such as low-bit quantization, robustness against attacks, and explainable models are being explored to address these challenges. In conclusion, deep generative models are revolutionizing communication by enabling semantic communication, which offers new possibilities for reducing data traffic and enhancing communication quality. The paper highlights the potential of these models in future communication frameworks and the challenges that need to be addressed for their successful implementation.Deep generative models are showing great potential in semantic communication, where the receiver regenerates content semantically consistent with the transmitted message rather than recovering the original bits. This paradigm shift offers new opportunities for reducing data traffic and enabling novel applications. The paper presents a unified perspective of deep generative models in semantic communication and their transformative role in future communication frameworks. It discusses the challenges and opportunities in developing generative models tailored for communication systems. Semantic communication involves three levels: effectiveness, semantic, and technical. The semantic level focuses on regenerating content that is semantically equivalent to the transmitted message. Generative models, such as Variational Autoencoders (VAEs), Flow-based Models, Generative Adversarial Networks (GANs), and Diffusion Models, are explored for their capabilities in semantic communication. These models can provide lossy or lossless compression, stable training, and fast sampling, making them suitable for communication tasks. Semantic conditioning is crucial for generative semantic communication, as it guides the generation process. Proper semantic extraction ensures that the receiver can regenerate content consistent with the transmitted message. However, extracting accurate semantic representations remains challenging, especially for multimodal data. The paper also discusses the evolution of communication tasks, including semantic compression beyond perceptual compression and modular architectures beyond end-to-end designs. Generative models can improve communication quality by denoising, restoring, and compressing data, even in the presence of channel errors. Emerging applications of generative models in semantic communication include semantic decomposition for channel-adaptive communication, multimodal semantic diversity for reliable communication, content creation, multi-user communications, network digital twins, multimodal generation, and personalized communication. These applications leverage the flexibility and adaptability of generative models to enhance communication systems. Challenges in deep generative models for semantic communication include computational efficiency, trustworthiness, and explainability. Solutions such as low-bit quantization, robustness against attacks, and explainable models are being explored to address these challenges. In conclusion, deep generative models are revolutionizing communication by enabling semantic communication, which offers new possibilities for reducing data traffic and enhancing communication quality. The paper highlights the potential of these models in future communication frameworks and the challenges that need to be addressed for their successful implementation.