text: This paper presents an immersive telepresence system for entertainment and meetings (ITEM). The system aims to provide a radically new video communication experience by seamlessly merging participants into the same virtual space to allow a natural interaction among them and shared collaborative contents. With the goal to make a scalable, flexible system for various business solutions as well as easily accessible by massive consumers, we address the challenges in the whole pipeline of media processing, communication, and displaying in our design and realization of such a system. Particularly, in this paper we focus on the system aspects that maximize the end-user experience, optimize the system and network resources, and enable various teleimmersive (TI) application scenarios. In addition, we also present a few key technologies, i.e., fast object-based video coding for real world data and spatialized audio capture and 3-D sound localization for group teleconferencing. Our effort is to investigate and optimize the key system components and provide an efficient end-to-end optimization and integration by considering user needs and preferences. Extensive experiments show the developed system runs reliably and comfortably in real time with a minimal setup requirement (e.g., a webcam or a color plus depth camera, an optional microphone array, a laptop desktop connected to the Internet) for TI communication. With such a really minimal deployment requirement, we present a variety of interesting applications and user experiences created by ITEM.
  @cite_4: This paper presents a lightweight tele-immersive video chat system named CuteChat. Based on our recently developed video object cutout technology, the CuteChat system is designed and optimized to provide a radically new video chat experience by merging each participant in the same shared space, allowing them to interact more naturally in an integrated manner. With the goal to make the system easily accessible by massive consumers, we address the challenges in the whole pipeline of video processing, coding, communication, composition, and playback. Extensive experiments have shown that the proposed CuteChat system runs reliably and comfortably in real time on one's laptop or desktop PC, and it needs only a commodity webcam for video acquisition and just public Internet for tele-immersive video conferencing. With such a really minimal deployment requirement, we present a variety of interesting applications and user experiences created by the CuteChat system.
  @cite_21: Foreground extraction for live video sequence is a challenging task in vision. Traditional methods often make various assumptions to simplify the problem, which make them less robust in real-world applications. Recently, Time-ofFlight (TOF) cameras provide a convenient way to sense the scene depth at video frame-rate. Compared to the appearance or motion cues, depth information is less sensitive to environment changes. Motivated by the fact that TOF cameras have not been widely used in video segmentation application, we in this paper investigate the problem of performing robust, real-time bi-layer segmentation using a TOF camera and propose an effective algorithm named TofCut. TofCut combines color and depth cues in a unified probabilistic fusion framework and a novel adaptive weighting scheme is employed to control the influence of these two cues intelligently. By comparing our segmentation results with ground truth data, we demonstrate the effectiveness of TofCut on an extensive set of experimental results.
  @cite_3: This paper presents an algorithm capable of real-time separation of foreground from background in monocular video sequences. Automatic segmentation of layers from colour contrast or from motion alone is known to be error-prone. Here motion, colour and contrast cues are probabilistically fused together with spatial and temporal priors to infer layers accurately and efficiently. Central to our algorithm is the fact that pixel velocities are not needed, thus removing the need for optical flow estimation, with its tendency to error and computational expense. Instead, an efficient motion vs nonmotion classifier is trained to operate directly and jointly on intensity-change and contrast. Its output is then fused with colour information. The prior on segmentation is represented by a second order, temporal, Hidden Markov Model, together with a spatial MRF favouring coherence except where contrast is high. Finally, accurate layer segmentation and explicit occlusion detection are efficiently achieved by binary graph cut. The segmentation accuracy of the proposed algorithm is quantitatively evaluated with respect to existing groundtruth data and found to be comparable to the accuracy of a state of the art stereo segmentation algorithm. Foreground background segmentation is demonstrated in the application of live background substitution and shown to generate convincingly good quality composite video.
  @cite_5: Mirrors are indispensable objects in our lives. The capability of simulating a mirror on a computer display, augmented with virtual scenes and objects, opens the door to many interesting and useful applications from fashion design to medical interventions. Realistic simulation of a mirror is challenging as it requires accurate viewpoint tracking and rendering, wide-angle viewing of the environment, as well as real-time performance to provide immediate visual feedback. In this paper, we propose a virtual mirror rendering system using a network of commodity structured-light RGB-D cameras. The depth information provided by the RGB-D cameras can be used to track the viewpoint and render the scene from different prospectives. Missing and erroneous depth measurements are common problems with structured-light cameras. A novel depth denoising and completion algorithm is proposed in which the noise removal and interpolation procedures are guided by the foreground background label at each pixel. The foreground background label is estimated using a probabilistic graphical model that considers color, depth, background modeling, depth noise modeling, and spatial constraints. The wide viewing angle of the mirror system is realized by combining the dynamic scene, captured by the static camera network with a 3-D background model created off-line, using a color-depth sequence captured by a movable RGB-D camera. To ensure a real-time response, a scalable client-and-server architecture is used with the 3-D point cloud processing, the viewpoint estimate, and the mirror image rendering are all done on the client side. The mirror image and the viewpoint estimate are then sent to the server for final mirror view synthesis and viewpoint refinement. Experimental results are presented to show the accuracy and effectiveness of each component and the entire system.
  @cite_12: This paper demonstrates the high quality, real-time segmentation techniques. We achieve real-time segmentation of foreground from background layers in stereo video sequences. Automatic separation of layers from colour contrast or from stereo alone is known to be error-prone. Here, colour, contrast and stereo matching information are fused to infer layers accurately and efficiently. The first algorithm, layered dynamic programming (LDP), solves stereo in an extended 6-state space that represents both foreground background layers and occluded regions. The stereo-match likelihood is then fused with a contrast-sensitive colour model that is learned on the fly, and stereo disparities are obtained by dynamic programming. The second algorithm, layered graph cut (LGC), does not directly solve stereo. Instead the stereo match likelihood is marginalised over foreground and background hypotheses, and fused with a contrast-sensitive colour model like the one used in LDP. Segmentation is solved efficiently by ternary graph cut. Both algorithms are evaluated with respect to ground truth data and found to have similar performance, substantially better than stereo or colour contrast alone. However, their characteristics with respect to computational efficiency are rather different. The algorithms are demonstrated in the application of background substitution and shown to give good quality composite video output.
summary: Though recent years have witnessed some important advances in the research field of foreground segmentation from a live video @cite_3 @cite_12 @cite_5 @cite_21 , the technology existing today is still distant from what a practical solution really desires, especially when it is put under a context of live video teleconferencing. Some known issues are small video resolutions e.g. @math , inaccurate foreground segmentation under challenging test conditions, and requiring stereo cameras @cite_12 or additional depth sensors @cite_21 . Recently, Lu et al @cite_4 have developed a more advanced segmentation technique to realize a practical 2D TI system. Without supporting a flexible setup of incorporating a depth camera when available, it does not handle challenging situations well by using only a single webcam (e.g. background foreground with very similar colors or difficult hand gestures). Furthermore, their system does not support multimodality and allows only limited interactions with shared contents, while the current design lacks capabilities to support many concurrent meeting sessions and flexibility in functionality extension in different application contexts.