  The burgeoning generative artificial intelligence technology offers novel
insights into the development of semantic communication (SemCom) frameworks.
These frameworks hold the potential to address the challenges associated with
the black-box nature inherent in existing end-to-end training manner for the
existing SemCom framework, as well as deterioration of the user experience
caused by the inevitable error floor in deep learning-based SemCom. In this
paper, we focus on the widespread remote monitoring scenario, and propose a
semantic change driven generative SemCom framework. Therein, the semantic
encoder and semantic decoder can be optimized independently. Specifically, we
develop a modular semantic encoder with value of information based semantic
sampling function. In addition, we propose a conditional denoising diffusion
probabilistic mode-assisted semantic decoder that relies on received semantic
information from the source, namely, the semantic map, and the local static
scene information to remotely regenerate scenes. Moreover, we demonstrate the
effectiveness of the proposed semantic encoder and decoder as well as the
considerable potential in reducing energy consumption through simulation based
on the realistic $\mathcal{F}$ composite channel fading model. The code is
available at https://github.com/wty2011jl/SCDGSC.git.
