With the development of real-time video conferences, interactive multimedia services have proliferated, leading to a surge in traffic. Interactivity becomes one of the main features on future multimedia services, which brings a new challenge to Computer Vision (CV) for communications. In addition, many directions for CV in video, like recognition, understanding, saliency segmentation, coding, and so on, do not satisfy the demands of the multiple tasks of interactivity without integration. Meanwhile, with the rapid development of the foundation models, we apply task-oriented semantic communications to handle them. Therefore, we propose a novel framework, called Real-Time Video Conference with Foundation Model (RTVCFM), to satisfy the requirement of interactivity in the multimedia service. Firstly, at the transmitter, we perform the causal understanding and spatiotemporal decoupling on interactive videos, with the Video Time-Aware Large Language Model (VTimeLLM), Iterated Integrated Attributions (IIA) and Segment Anything Model 2 (SAM2), to accomplish the video semantic segmentation. Secondly, in the transmission, we propose a two-stage semantic transmission optimization driven by Channel State Information (CSI), which is also suitable for the weights of asymmetric semantic information in real-time video, so that we achieve a low bit rate and high semantic fidelity in the video transmission. Thirdly, at the receiver, RTVCFM provides multidimensional fusion with the whole semantic segmentation by using the Diffusion Model for Foreground Background Fusion (DMFBF), and then we reconstruct the video streams. Finally, the simulation result demonstrates that RTVCFM can achieve a compression ratio as high as 95.6%, while it guarantees high semantic similarity of 98.73% in Multi-Scale Structural Similarity Index Measure (MS-SSIM) and 98.35% in Structural Similarity (SSIM), which shows that the reconstructed video is relatively similar to the original video.

With the rapid development of video conferencing technology, interactive multimedia services proliferate, resulting in a surge in business traffic. Remote work and online collaboration will become the mainstream office in the future, which poses new challenges to computer vision technology in the field of communication. At the same time, the appearance of semantic communication has solved the problem that the traditional video coding technology can not deal with semantic redundancy. Therefore, we propose the Semantic Transmission Optimization of Video Conference(STOVC) architecture to meet the interactivity requirements of multimedia services. We first use Iterated Integrated Attribution(IIA) and Segment Anything Model 2(SAM2) to understand the video and complete the semantic segmentation of the video. We then propose Neural Video Compression with Actor-Critic(NVCAC) to optimize video encoding and transmission strategies to balance video compression with semantic fidelity. Finally, the$\alpha$-fusion method is used to reconstruct the video at the receiving end. In addition, we use DeepCache accelerated stable diffusion to generate conference video to enrich the data set. The experimental results show that STOVC has a better performance than H.264 and H.265 at low bit rate.