Semantic communication has recently attracted significant interest from both
industry and academia due to its potential to transform the existing
data-focused communication architecture towards a more generally intelligent
and goal-oriented semantic-aware networking system. Despite its promising
potential, semantic communications and semantic-aware networking are still at
their infancy. Most existing works focus on transporting and delivering the
explicit semantic information, e.g., labels or features of objects, that can be
directly identified from the source signal. The original definition of
semantics as well as recent results in cognitive neuroscience suggest that it
is the implicit semantic information, in particular the hidden relations
connecting different concepts and feature items that plays the fundamental role
in recognizing, communicating, and delivering the real semantic meanings of
messages. Motivated by this observation, we propose a novel reasoning-based
implicit semantic-aware communication network architecture that allows multiple
tiers of CDC and edge servers to collaborate and support efficient semantic
encoding, decoding, and interpretation for end-users. We introduce a new
multi-layer representation of semantic information taking into consideration
both the hierarchical structure of implicit semantics as well as the
personalized inference preference of individual users. We model the semantic
reasoning process as a reinforcement learning process and then propose an
imitation-based semantic reasoning mechanism learning (iRML) solution for the
edge servers to leaning a reasoning policy that imitates the inference behavior
of the source user. A federated GCN-based collaborative reasoning solution is
proposed to allow multiple edge servers to jointly construct a shared semantic
interpretation model based on decentralized knowledge datasets.

本文提出了一种基于推理的隐含语义感知通信网络架构，允许多个 CDC 和边缘服务器协作支持端到端的语义编码，解码和解释，建立多层次表示和个人化推理优先权，并采用强化学习和模仿学习算法进行协作推理。

基于模仿学习的隐式语义感知通讯网络：多层表示与协作推理

Imitation Learning-based Implicit Semantic-aware Communication Networks: Multi-layer Representation and Collaborative Reasoning

This paper proposes a new method for live free-viewpoint human performance
capture with dynamic details (e.g., cloth wrinkles) using a single RGBD camera.
Our main contributions are: (i) a multi-layer representation of garments and
body, and (ii) a physics-based performance capture procedure. We first digitize
the performer using multi-layer surface representation, which includes the
undressed body surface and separate clothing meshes. For performance capture,
we perform skeleton tracking, cloth simulation, and iterative depth fitting
sequentially for the incoming frame. By incorporating cloth simulation into the
performance capture pipeline, we can simulate plausible cloth dynamics and
cloth-body interactions even in the occluded regions, which was not possible in
previous capture methods. Moreover, by formulating depth fitting as a physical
process, our system produces cloth tracking results consistent with the depth
observation while still maintaining physical constraints. Results and
evaluations show the effectiveness of our method. Our method also enables new
types of applications such as cloth retargeting, free-viewpoint video rendering
and animations.

本文提出了一种新的方法，使用单个 RGBD 摄像机进行实时自由视点的人体表演捕捉，主要内容包括：多层服装和身体的表示法，以及基于物理的表演捕捉过程。