Integrated sensing and communication (ISAC) increasingly exposes a gap in today's channel modeling. Efficient statistical models focus on coarse communication-centric metrics, and therefore miss the weak but critical multipath signatures for sensing, whereas deterministic models are computationally inefficient to scale for system-level ISAC evaluation. This gap calls for a unifying abstraction that can couple what the environment means for sensing with how the channel behaves for communication, namely, environmental semantics. This article clarifies the meaning and essentiality of environmental semantics in ISAC channel modeling and establishes how semantics is connected to observable channel structures across multiple semantic levels. Based on this perspective, a semantics-oriented channel modeling principle was advocated, which preserves environmental semantics while abstracting unnecessary detail to balance accuracy and complexity. Then, a generative AI-empowered semantic twin channel model (STCM) was introduced to generate a family of physically plausible channel realizations representative of a semantic condition. Case studies further show semantic consistency under challenging multi-view settings, suggesting a practical path to controllable simulation, dataset generation, and reproducible ISAC benchmarking toward future design and standardization.

翻译：集成感知与通信（ISAC）日益凸显出现有信道建模的不足。高效的统计模型聚焦于粗粒度的通信中心指标，因而遗漏了感知所需微弱但关键的多径特征；而确定性模型在计算效率上难以扩展至系统级ISAC评估。这一鸿沟亟需一种能耦合环境对感知的意义与信道对通信行为的统一抽象，即环境语义。本文阐释了环境语义在ISAC信道建模中的内涵与必要性，并建立了语义在多层级上与可观测信道结构的关联机制。基于此视角，我们提出以语义为导向的信道建模原则，在保留环境语义的同时抽象非必要细节，以平衡精度与复杂度。随后，引入生成式AI赋能的语义孪生信道模型（STCM），该模型能生成代表特定语义条件的物理可信信道实例族。案例研究进一步展示了在挑战性多视角设置下的语义一致性，为可控仿真、数据集生成及可复现的ISAC基准测试提供了通向未来设计与标准化的可行路径。