Generative AI Meets Semantic Communication: Evolution and Revolution of
  Communication Tasks
Eleonora Grassucci Jihong Park Sergio Barbarossa Seong-Lyun Kim Jinho Choi Danilo Comminiello
While deep generative models are showing exciting abilities in computer
vision and natural language processing, their adoption in communication
frameworks is still far underestimated. These methods are demonstrated to
evolve solutions to classic communication problems such as denoising,
restoration, or compression. Nevertheless, generative models can unveil their
real potential in semantic communication frameworks, in which the receiver is
not asked to recover the sequence of bits used to encode the transmitted
(semantic) message, but only to regenerate content that is semantically
consistent with the transmitted message. Disclosing generative models
capabilities in semantic communication paves the way for a paradigm shift with
respect to conventional communication systems, which has great potential to
reduce the amount of data traffic and offers a revolutionary versatility to
novel tasks and applications that were not even conceivable a few years ago. In
this paper, we present a unified perspective of deep generative models in
semantic communication and we unveil their revolutionary role in future
communication frameworks, enabling emerging applications and tasks. Finally, we
analyze the challenges and opportunities to face to develop generative models
specifically tailored for communication systems.