HeadOn: Real-time Reenactment of Human Portrait Videos

We propose HeadOn, the first real-time source-to-target reenactment approach for complete human portrait videos that enables transfer of torso and head motion, face expression, and eye gaze. Given a short RGB-D video of the target actor, we automatically construct a personalized geometry proxy that embeds a parametric head, eye, and kinematic torso model. A novel real-time reenactment algorithm employs this proxy to photo-realistically map the captured motion from the source actor to the target actor. On top of the coarse geometric proxy, we propose a video-based rendering technique that composites the modified target portrait video via view- and pose-dependent texturing, and creates photo-realistic imagery of the target actor under novel torso and head poses, facial expressions, and gaze directions. To this end, we propose a robust tracking of the face and torso of the source actor. We extensively evaluate our approach and show significant improvements in enabling much greater flexibility in creating realistic reenacted output videos.Comment: Video: https://www.youtube.com/watch?v=7Dg49wv2c_g Presented at Siggraph'1

Bringing Telepresence to Every Desk

In this paper, we work to bring telepresence to every desktop. Unlike commercial systems, personal 3D video conferencing systems must render high-quality videos while remaining financially and computationally viable for the average consumer. To this end, we introduce a capturing and rendering system that only requires 4 consumer-grade RGBD cameras and synthesizes high-quality free-viewpoint videos of users as well as their environments. Experimental results show that our system renders high-quality free-viewpoint videos without using object templates or heavy pre-processing. While not real-time, our system is fast and does not require per-video optimizations. Moreover, our system is robust to complex hand gestures and clothing, and it can generalize to new users. This work provides a strong basis for further optimization, and it will help bring telepresence to every desk in the near future. The code and dataset will be made available on our website https://mcmvmc.github.io/PersonalTelepresence/

Reinventing a teleconferencing system

Thesis (S.M.)--Massachusetts Institute of Technology, Program in Media Arts & Sciences, 2001.Includes bibliographical references (p. 67-71).In looking forward to more natural we can anticipate that the teleconferencing system of the future will enable participants at distant locations to share the same virtual space. The visual object of each participant can be transmitted to the other sites and be rendered from an individual perspective. This thesis presents an effort, X-Conference, to reinvent a teleconferencing system toward the concept of "3-D Virtual Teleconferencing." Several aspects are explored. A multiple-camera calibration approach is implemented and is employed to effectively blend the real view and the virtual view. An individualized 3-D head object is built semi-automatically by mapping the real texture to the globally modified generic model. Head motion parameters are extracted from tracking artificial and/or facial features. Without using the articulation model, facial animation is partially achieved by using texture displacement. UDP/IP multicast and TCP/IP unicast are both utilized to implement the networking scheme.by Xin Wang.S.M

Loss-resilient Coding of Texture and Depth for Free-viewpoint Video Conferencing

Free-viewpoint video conferencing allows a participant to observe the remote 3D scene from any freely chosen viewpoint. An intermediate virtual viewpoint image is commonly synthesized using two pairs of transmitted texture and depth maps from two neighboring captured viewpoints via depth-image-based rendering (DIBR). To maintain high quality of synthesized images, it is imperative to contain the adverse effects of network packet losses that may arise during texture and depth video transmission. Towards this end, we develop an integrated approach that exploits the representation redundancy inherent in the multiple streamed videos a voxel in the 3D scene visible to two captured views is sampled and coded twice in the two views. In particular, at the receiver we first develop an error concealment strategy that adaptively blends corresponding pixels in the two captured views during DIBR, so that pixels from the more reliable transmitted view are weighted more heavily. We then couple it with a sender-side optimization of reference picture selection (RPS) during real-time video coding, so that blocks containing samples of voxels that are visible in both views are more error-resiliently coded in one view only, given adaptive blending will erase errors in the other view. Further, synthesized view distortion sensitivities to texture versus depth errors are analyzed, so that relative importance of texture and depth code blocks can be computed for system-wide RPS optimization. Experimental results show that the proposed scheme can outperform the use of a traditional feedback channel by up to 0.82 dB on average at 8% packet loss rate, and by as much as 3 dB for particular frames

Fast and Accurate Depth Estimation from Sparse Light Fields

We present a fast and accurate method for dense depth reconstruction from sparsely sampled light fields obtained using a synchronized camera array. In our method, the source images are over-segmented into non-overlapping compact superpixels that are used as basic data units for depth estimation and refinement. Superpixel representation provides a desirable reduction in the computational cost while preserving the image geometry with respect to the object contours. Each superpixel is modeled as a plane in the image space, allowing depth values to vary smoothly within the superpixel area. Initial depth maps, which are obtained by plane sweeping, are iteratively refined by propagating good correspondences within an image. To ensure the fast convergence of the iterative optimization process, we employ a highly parallel propagation scheme that operates on all the superpixels of all the images at once, making full use of the parallel graphics hardware. A few optimization iterations of the energy function incorporating superpixel-wise smoothness and geometric consistency constraints allows to recover depth with high accuracy in textured and textureless regions as well as areas with occlusions, producing dense globally consistent depth maps. We demonstrate that while the depth reconstruction takes about a second per full high-definition view, the accuracy of the obtained depth maps is comparable with the state-of-the-art results.Comment: 15 pages, 15 figure

View Synthesis of Dynamic Scenes based on Deep 3D Mask Volume

Image view synthesis has seen great success in reconstructing photorealistic visuals, thanks to deep learning and various novel representations. The next key step in immersive virtual experiences is view synthesis of dynamic scenes. However, several challenges exist due to the lack of high-quality training datasets, and the additional time dimension for videos of dynamic scenes. To address this issue, we introduce a multi-view video dataset, captured with a custom 10-camera rig in 120FPS. The dataset contains 96 high-quality scenes showing various visual effects and human interactions in outdoor scenes. We develop a new algorithm, Deep 3D Mask Volume, which enables temporally-stable view extrapolation from binocular videos of dynamic scenes, captured by static cameras. Our algorithm addresses the temporal inconsistency of disocclusions by identifying the error-prone areas with a 3D mask volume, and replaces them with static background observed throughout the video. Our method enables manipulation in 3D space as opposed to simple 2D masks, We demonstrate better temporal stability than frame-by-frame static view synthesis methods, or those that use 2D masks. The resulting view synthesis videos show minimal flickering artifacts and allow for larger translational movements

Rendering and display for multi-viewer tele-immersion

Video teleconferencing systems are widely deployed for business, education and personal use to enable face-to-face communication between people at distant sites. Unfortunately, the two-dimensional video of conventional systems does not correctly convey several important non-verbal communication cues such as eye contact and gaze awareness. Tele-immersion refers to technologies aimed at providing distant users with a more compelling sense of remote presence than conventional video teleconferencing. This dissertation is concerned with the particular challenges of interaction between groups of users at remote sites. The problems of video teleconferencing are exacerbated when groups of people communicate. Ideally, a group tele-immersion system would display views of the remote site at the right size and location, from the correct viewpoint for each local user. However, is is not practical to put a camera in every possible eye location, and it is not clear how to provide each viewer with correct and unique imagery. I introduce rendering techniques and multi-view display designs to support eye contact and gaze awareness between groups of viewers at two distant sites. With a shared 2D display, virtual camera views can improve local spatial cues while preserving scene continuity, by rendering the scene from novel viewpoints that may not correspond to a physical camera. I describe several techniques, including a compact light field, a plane sweeping algorithm, a depth dependent camera model, and video-quality proxies, suitable for producing useful views of a remote scene for a group local viewers. The first novel display provides simultaneous, unique monoscopic views to several users, with fewer user position restrictions than existing autostereoscopic displays. The second is a random hole barrier autostereoscopic display that eliminates the viewing zones and user position requirements of conventional autostereoscopic displays, and provides unique 3D views for multiple users in arbitrary locations