  End-to-end visual communication systems typically optimize a trade-off
between channel bandwidth costs and signal-level distortion metrics. However,
under challenging physical conditions, this traditional discriminative
communication paradigm often results in unrealistic reconstructions with
perceptible blurring and aliasing artifacts, despite the inclusion of
perceptual or adversarial losses for optimizing. This issue primarily stems
from the receiver's limited knowledge about the underlying data manifold and
the use of deterministic decoding mechanisms. To address these limitations,
this paper introduces DiffCom, a novel end-to-end generative communication
paradigm that utilizes off-the-shelf generative priors and probabilistic
diffusion models for decoding, thereby improving perceptual quality without
heavily relying on bandwidth costs and received signal quality. Unlike
traditional systems that rely on deterministic decoders optimized solely for
distortion metrics, our DiffCom leverages raw channel-received signal as a
fine-grained condition to guide stochastic posterior sampling. Our approach
ensures that reconstructions remain on the manifold of real data with a novel
confirming constraint, enhancing the robustness and reliability of the
generated outcomes. Furthermore, DiffCom incorporates a blind posterior
sampling technique to address scenarios with unknown forward transmission
characteristics. Extensive experimental validations demonstrate that DiffCom
not only produces realistic reconstructions with details faithful to the
original data but also achieves superior robustness against diverse wireless
transmission degradations. Collectively, these advancements establish DiffCom
as a new benchmark in designing generative communication systems that offer
enhanced robustness and generalization superiorities.
