Semantic communication (SC) emerges as a breakthrough paradigm for efficient data transmission in next-generation communication networks. However, SC is still in the infant stage with quite a few limitations, such as insufficient semantic representation capacity, high communication latency, and the susceptibility to channel noise. In this paper, we proposeLiFiSC, a cross-modal fusion based generative semantic communication framework with strong semantic compression capacity and high-fidelity semantic restoration. We then extend it toLiFiSCvv, specifically designed to achieve 3D volumetric video transmission and reconstruction with photorealistic visual quality and pixel-level visual consistency, providing end-to-end live watching experience with acceptable latency. We innovatively incorporate unified vision-language encoding into semantic communication, achieving superior semantic understanding and compression.LiFiSCvvcomprises three key components: (1) Information redundancy reduction through lightweight video analysis and structure-from-motion techniques, decreasing reconstruction cost; (2) Cross-modal fusion learning driven codec mechanisms that enable efficient semantic representation and robust transmission against channel impairments; and (3) Feed-forward vision transformer for rapid volumetric video reconstruction and rendering. Comprehensive evaluation results demonstrate thatLiFiSCvvachieves a live high-fidelity and visually consistent video watching experience, with only seconds of end-to-end latency and around 42× semantic compression, significantly outperforming SOTA methods in general image and volumetric video transmission. The project page is available here: https://inml-tygong.github.io/LiFiSCvv/.

Federated learning (FL) stands out as a promising solution to address the ever-increasing data scarcity problem of large language model (LLM) training through collecting data from distributed sources. The unique challenges however exist in three aspects, the extreme large communication overhead due to frequent model aggregation, the suboptimal performance due to data heterogeneity, and privacy leakage risk due to model parameter exchange. To address these issues, we propose Federated Knowledge Learning (FKL), a novel token-level knowledge sharing framework tailored for LLM to achieve effective task relevant knowledge aggregation. FKL offers the following advantages: (1) Adaptive Flexibility: Clients and servers can employ models customized for their specific tasks while ensuring seamless collaboration. Each node within the system functions independently and can be flexibly adapted as needed. (2) Communication Efficiency: Instead of huge model or parameter exchange, the unique knowledge sharing mechanism of FKL can significantly reduce the communication overhead while delivering superior performance. (3) Privacy Safeguards: The distributed token-level knowledge-sharing mechanism ensures privacy by deidentifying sensitive information and mitigating potential leakage risks. Experiments on LLMs (125M-7B parameters) validate FKL's versatility, achieving 50% higher accuracy on GLUE benchmarks and a notable 0.7x improvement in privacy task response quality compared to existing FL-based LLM approaches.

The increasing demand for augmented and virtual reality applications has highlighted the importance of crafting immersive 3D scenes from a simple single-view image. However, due to the partial priors provided by single-view input, existing methods are often limited to reconstruct low-consistency 3D scenes with narrow fields of view from single-view input. These limitations make them less capable of generalizing to reconstruct immersive scenes. To address this problem, we propose ExScene, a two-stage pipeline to reconstruct an immersive 3D scene from any given single-view image. ExScene designs a novel multimodal diffusion model to generate a high-fidelity and globally consistent panoramic image. We then develop a panoramic depth estimation approach to calculate geometric information from panorama, and we combine geometric information with high-fidelity panoramic image to train an initial 3D Gaussian Splatting (3DGS) model. Following this, we introduce a GS refinement technique with 2D stable video diffusion priors. We add camera trajectory consistency and color-geometric priors into the denoising process of diffusion to improve color and spatial consistency across image sequences. These refined sequences are then used to fine-tune the initial 3DGS model, leading to better reconstruction quality. Experimental results demonstrate that our ExScene achieves consistent and immersive scene reconstruction using only single-view input, significantly surpassing state-of-the-art baselines.

Embodied AI has notably advanced the autonomy of physical agents such as robots, vehicles, and AAVs, expanding their application scope. However, existing systems are predominantly data-driven, relying on static programming and pre-trained models. This limits their adaptability to dynamic and unforeseen scenarios. Additionally, the high computational cost of training large-scale models locally hinders their practical deployment. One promising solution lies in integrating Large Language Models (LLMs) into Embodied AI frameworks. Although LLMs excel in reasoning, coding, and perception, most existing frameworks adopt a single-LLM architecture, which restricts their effectiveness in addressing complex, multimodal tasks. The diverse strengths of individual LLMs, ranging from natural language understanding, visual processing to code generation, are seldom utilized in a collaborative and structured manner. To address these challenges, we propose a knowledge-driven framework, called EUF, that incorporates multi-LLMs into the Embodied AI architecture for AAV-as-a-Service in Intelligent Transportation Systems. Each LLM is dedicated to a specific stage of the AAV task, including user intent interpretation, adaptive path planning with code generation, and error-feedback mechanisms. Our research explores both One-shot and Segmented Code Generation approaches using various LLM-driven models to identify the optimal strategy. We conduct an in-depth analysis of different code errors to evaluate the strengths and limitations of each approach, including the feedback capabilities of the LLM-driven models. Experiments conducted in the AirSim environment demonstrate the framework’s robustness and accuracy in complex AAV path planning tasks, highlighting its practical potential for real-world ITS deployments.