arXiv:2410.22112v1 Announce Type: new
Abstract: This paper studies an efficient multimodal data communication scheme for video conferencing. In our considered system, a speaker gives a talk to the audiences, with talking head video and audio being transmitted. Since the speaker does not frequently change posture and high-fidelity transmission of audio (speech and music) is required, redundant visual video data exists and can be removed by generating the video from the audio. To this end, we propose a wave-to-video (Wav2Vid) system, an efficient video transmission framework that reduces transmitted data by generating talking head video from audio. In particular, full-duration audio and short-duration video data are synchronously transmitted through a wireless channel, with neural networks (NNs) extracting and encoding audio and video semantics. The receiver then combines the decoded audio and video data, as well as uses a generative adversarial network (GAN) based model to generate the lip movement videos of the speaker. Simulation results show that the proposed Wav2Vid system can reduce the amount of transmitted data by up to 83% while maintaining the perceptual quality of the generated conferencing video.
Analyzing an Efficient Multimodal Data Communication Scheme for Video Conferencing
The study presented in this paper focuses on the development of an efficient multimodal data communication scheme for video conferencing. In today’s world, video conferencing has become increasingly popular, and it is important to optimize the transmission of video and audio data to deliver a seamless and high-quality communication experience.
The research specifically looks into the scenario where a speaker is giving a talk to an audience through video conferencing. In such cases, the speaker’s posture does not significantly change, and the primary focus is on transmitting high-fidelity audio. Due to the relative stability of the speaker’s visual representation, there exists redundant visual video data that can be eliminated by generating the video from the audio signal.
This concept of generating video from audio is where the proposed wave-to-video (Wav2Vid) system comes into play. The Wav2Vid system is designed to efficiently transmit video data by extracting and encoding the audio and video semantics using neural networks (NNs). The video is generated by combining the decoded audio and video data at the receiver’s end, and a generative adversarial network (GAN) based model is used to generate accurate lip movement videos of the speaker.
The key advantage of the Wav2Vid system is its ability to significantly reduce the amount of transmitted data, up to 83%, while maintaining the perceptual quality of the generated conferencing video. This reduction in data transmission has implications for bandwidth usage, especially in situations where network resources might be limited or expensive.
The research presented in this paper is a prime example of the multi-disciplinary nature of multimedia information systems. It combines principles from signal processing, machine learning, and computer vision to develop an innovative solution for optimizing video conferencing. This approach highlights the importance of integrating various disciplines to address complex challenges in the field.
Furthermore, the concept of generating video from audio has implications beyond video conferencing. It can be applied to various multimedia applications such as animations, artificial reality, augmented reality, and virtual realities. By eliminating redundant visual data and generating visuals from audio signals, it opens up possibilities for efficient content generation and transmission in these domains.
In conclusion, the proposed Wav2Vid system presents an efficient multimodal data communication scheme for video conferencing. Its ability to reduce data transmission while maintaining perceptual quality is a valuable contribution to the field. The research also demonstrates the interdisciplinary nature of multimedia information systems and highlights the potential applications of generating visuals from audio signals in various multimedia domains.