The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform

Rolvole Lindsetmo - One of the best experts on this subject based on the ideXlab platform.

Christoph Meinel - One of the best experts on this subject based on the ideXlab platform.

  • full body webrtc Video Conferencing in a web based real time collaboration system
    Computer Supported Cooperative Work in Design, 2016
    Co-Authors: Matthias Wenzel, Christoph Meinel
    Abstract:

    Remote collaboration systems are a necessity for geographically dispersed teams in achieving a common goal. Real-time groupware systems frequently provide a shared workspace where users interact with shared artifacts. However, a shared workspace is often not enough for maintaining the awareness of other users. Video Conferencing can create a visual context simplifying the user's communication and understanding. In addition, flexible working modes and modern communication systems allow users to work at any time at any location. It is therefore desirable that a groupware system can run on users' everyday devices, such as smartphones and tablets, in the same way as on traditional desktop hardware. We present a standards compliant, web browser-based real-time remote collaboration system that includes WebRTC-based Video Conferencing. It allows a full-body Video setup where everyone can see what other participants are doing and where they are pointing in the shared workspace. In contrast to standard WebRTC's peer-to-peer architecture, our system implements a star topology WebRTC Video Conferencing. In this way, our solution improves network bandwidth efficiency from a linear to a constant network upstream consumption.

Tackdon Han - One of the best experts on this subject based on the ideXlab platform.

  • pami projection augmented meeting interface for Video Conferencing
    ACM Multimedia, 2018
    Co-Authors: Inhwan Kim, Junghyun Byun, Yoonsik Yang, Yoon Jung Park, Seungho Chae, Tackdon Han
    Abstract:

    Video Conferencing, which helps gather opinions and make decisions quickly among employees who are not in the same location, is now a very important communication tool in the workplace. Our research is one of these Video Conferencing solutions, specifically proposed to address the difficulties of analog materials sharing and feedback, and has added some useful features for the smooth use of conference participants. We conducted a comparative experiment on our proposed method of file sharing and the method that we had previously used in Video Conferencing. As a result, the proposed system yielded better results in terms of time and usability during a full-scale collaborative situation in which feedback was provided.

Mingyu Liu - One of the best experts on this subject based on the ideXlab platform.

  • one shot free view neural talking head synthesis for Video Conferencing
    Computer Vision and Pattern Recognition, 2020
    Co-Authors: Tingchun Wang, Arun Mallya, Mingyu Liu
    Abstract:

    We propose a neural talking-head Video synthesis model and demonstrate its application to Video Conferencing. Our model learns to synthesize a talking-head Video using a source image containing the target person's appearance and a driving Video that dictates the motion in the output. Our motion is encoded based on a novel keypoint representation, where the identity-specific and motion-related information is decomposed unsupervisedly. Extensive experimental validation shows that our model outperforms competing methods on benchmark datasets. Moreover, our compact keypoint representation enables a Video Conferencing system that achieves the same visual quality as the commercial H.264 standard while only using one-tenth of the bandwidth. Besides, we show our keypoint representation allows the user to rotate the head during synthesis, which is useful for simulating face-to-face Video Conferencing experiences.

Liu Ming-yu - One of the best experts on this subject based on the ideXlab platform.

  • One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing
    2021
    Co-Authors: Wang Ting-chun, Mallya Arun, Liu Ming-yu
    Abstract:

    We propose a neural talking-head Video synthesis model and demonstrate its application to Video Conferencing. Our model learns to synthesize a talking-head Video using a source image containing the target person's appearance and a driving Video that dictates the motion in the output. Our motion is encoded based on a novel keypoint representation, where the identity-specific and motion-related information is decomposed unsupervisedly. Extensive experimental validation shows that our model outperforms competing methods on benchmark datasets. Moreover, our compact keypoint representation enables a Video Conferencing system that achieves the same visual quality as the commercial H.264 standard while only using one-tenth of the bandwidth. Besides, we show our keypoint representation allows the user to rotate the head during synthesis, which is useful for simulating face-to-face Video Conferencing experiences.Comment: CVPR 2021 camera ready (oral). Our project page can be found at https://nvlabs.github.io/face-vid2vi