  This paper investigates the transmission of three-dimensional (3D) human face
content for immersive communication over a rate-constrained
transmitter-receiver link. We propose a new framework named NeRF-SeCom, which
leverages neural radiance fields (NeRF) and semantic communications to improve
the quality of 3D visualizations while minimizing the communication overhead.
In the NeRF-SeCom framework, we first train a NeRF face model based on the
NeRFBlendShape method, which is pre-shared between the transmitter and receiver
as the semantic knowledge base to facilitate the real-time transmission. Next,
with knowledge base, the transmitter extracts and sends only the essential
semantic features for the receiver to reconstruct 3D face in real time. To
optimize the transmission efficiency, we classify the expression features into
static and dynamic types. Over each video chunk, static features are
transmitted once for all frames, whereas dynamic features are transmitted over
a portion of frames to adhere to rate constraints. Additionally, we propose a
feature prediction mechanism, which allows the receiver to predict the dynamic
features for frames that are not transmitted. Experiments show that our
proposed NeRF-SeCom framework significantly outperforms benchmark methods in
delivering high-quality 3D visualizations of human faces.
