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Telecollaboration involves the teleportation of a remote collaborator to another real-world environment where their partner is located. The fidelity of the environment plays an important role for allowing corresponding spatial references in remote collaboration. We present a novel asymmetric platform, Augmented Virtual Teleportation (AVT), which provides high-fidelity telepresence of a remote VR user ( VR-Traveler ) into a real-world collaboration space to interact with a local AR user ( AR-Host ). AVT uses a 360° video camera (360-camera) that captures and live-streams the omni-directional scenes over a network. The remote VR-Traveler watching the video in a VR headset experiences live presence and co-presence in the real-world collaboration space. The VR-Traveler's movements are captured and transmitted to a 3D avatar overlaid onto the 360-camera which can be seen in the AR-Host's display. The visual and audio cues for each collaborator are synchronized in the Mixed Reality Collaboration space (MRC-space), where they can interactively edit virtual objects and collaborate in the real environment using the real objects as a reference. High fidelity, real-time rendering of virtual objects and seamless blending into the real scene allows for unique mixed reality use-case scenarios. Our working prototype has been tested with a user study to evaluate spatial presence, co-presence, and user satisfaction during telecollaboration. Possible applications of AVT are identified and proposed to guide future usage.

Path tracing provides photo-realistic rendering in many applications but intermediate previsualization often suffers from distracting noise. Since the fundamental underlying problem is insufficient samples, we exploit the coherence of the visual signal to reconstruct missing samples, using a low-rank matrix completion framework. We present novel methods to construct low rank matrices for incomplete images including missing pixel, missing sub-pixel, and multi-frame scenarios. A convolutional neural network provides fast pre-completion for initialising missing values, and subsequent weighted nuclear norm minimisation (WNNM) with a parameter adjustment strategy (PAWNNM) efficiently recovers missing values even in high frequency details. The result shows better visual quality than recent methods including compressed sensing based reconstruction.

This paper presents a novel immersive system called MR360 that provides interactive mixed reality (MR) experiences using a conventional low dynamic range (LDR) 360° panoramic video (360-video) shown in head mounted displays (HMDs). MR360 seamlessly composites 3D virtual objects into a live 360-video using the input panoramic video as the lighting source to illuminate the virtual objects. Image based lighting (IBL) is perceptually optimized to provide fast and believable results using the LDR 360-video as the lighting source. Regions of most salient lights in the input panoramic video are detected to optimize the number of lights used to cast perceptible shadows. Then, the areas of the detected lights adjust the penumbra of the shadow to provide realistic soft shadows. Finally, our real-time differential rendering synthesizes illumination of the virtual 3D objects into the 360-video. MR360 provides the illusion of interacting with objects in a video, which are actually 3D virtual objects seamlessly composited into the background of the 360-video. MR360 was implemented in a commercial game engine and tested using various 360-videos. Since our MR360 pipeline does not require any pre-computation, it can synthesize an interactive MR scene using a live 360-video stream while providing realistic high performance rendering suitable for HMDs.

This paper describes a complete system to create anatomically accurate example-based volume deformation and animation of articulated body regions, starting from multiple in vivo volume scans of a specific individual. In order to solve the correspondence problem across volume scans, a template volume is registered to each sample. The wide range of pose variations is first approximated by volume blend deformation (VBD), providing proper initialization of the articulated subject in different poses. A novel registration method is presented to efficiently reduce the computation cost while avoiding strong local minima inherent in complex articulated body volume registration. The algorithm highly constrains the degrees of freedom and search space involved in the nonlinear optimization, using hierarchical volume structures and locally constrained deformation based on the biharmonic clamped spline. Our registration step establishes a correspondence across scans, allowing a data-driven deformation approach in the volume domain. The results provide an occlusion-free person-specific 3D human body model, asymptotically accurate inner tissue deformations, and realistic volume animation of articulated movements driven by standard joint control estimated from the actual skeleton. Our approach also addresses the practical issues arising in using scans from living subjects. The robustness of our algorithms is tested by their applications on the hand, probably the most complex articulated region in the body, and the knee, a frequent subject area for medical imaging due to injuries.

WPSD (Weighted Pose Space Deformation) is an example based skinning method for articulated body animation. The per-vertex computation required in WPSD can be parallelized in a SIMD (Single Instruction Multiple Data) manner and implemented on a GPU. While such vertex-parallel computation is often done on the GPU vertex processors, further parallelism can potentially be obtained by using the fragment processors. In this paper, we develop a parallel deformation method using the GPU fragment processors. Joint weights for each vertex are automatically calculated from sample poses, thereby reducing manual effort and enhancing the quality of WPSD as well as SSD (Skeletal Subspace Deformation). We show sufficient speed-up of SSD, PSD (Pose Space Deformation) and WPSD to make them suitable for real-time applications.