Holoportation: Virtual 3D Teleportation in Real-time

By Sergio Orts, Christoph Rhemann, Sean Ryan Fanello, Wayne Chang, Adarsh Kowdle, Yu.G. Degtyarev, David Kim, Philip L. Davidson, Sameh Khamis, Mingsong Dou and 13 more. ACM Symposium on User Interface Software and Technology, 2016

743 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Cognitive Neuroscience, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/dd137c18c302ba7a25fe48aeb60a6b46a0e82189

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In Aerospace Engineering

In Cognitive Neuroscience

In Computer Vision and Pattern Recognition

Citations in the corpus

Mixture of volumetric primitives for efficient neural rendering (2021, background): “In addition, their high computational cost limits applicability to real-time applications, such as VR telepresence [Orts-Escolano et al. 2016; Wei et al. 2019].”
DeepHuman: 3D Human Reconstruction From a Single Image (2019, background): “Image-based reconstruction of a human body is an important research topic for VR/AR content creation [7], image and video editing and re-enactment [19, 43], holoportation [40] and virtual dressing [42].”
Deep Volumetric Video From Very Sparse Multi-view Performance Capture (2018, background): “Orts-Escolano et al. [2] employ active stereo cameras and highly specialized acquisition devices for real-time high-quality capture.”
Mini-Me: An Adaptive Avatar for Mixed Reality Remote Collaboration (2018, methodology): “Also, recent work in telepresence has demonstrated lifelike full-body reconstructions of distant persons, placed in a local environment, and viewed through AR [27, 34, 36].”
Dynamic View Synthesis from Dynamic Monocular Video (2021, methodology): “It facilitates applications such as replaying professional sports events in 3D [7], creating cinematic effects like freeze-frame bullet-time (from the movie “The Matrix”), virtual reality [11, 5], and virtual 3D teleportation [37].”
StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth Prediction (2018, background): “Active depth sensors like the Microsoft Kinect provide high quality depthmaps and have not only revolutionized computer vision research [12, 11, 41, 16, 55], but also play an important role in consumer level applications.”
Detailed, Accurate, Human Shape Estimation from Clothed 3D Scan Sequences (2017, background): “Other work is purely model free and attempts to estimate non-rigid 3D shape over time [41, 22, 11, 25].”
Learning to Reconstruct People in Clothing From a Single RGB Camera (2019, background): “from a low number of video cameras [60], or using one or more depth cameras, either specifically for the human body [9, 77, 84] or for general free-form surfaces [88, 51, 54, 37, 26, 70].”
Multi-Garment Net: Learning to Dress 3D People From Images (2019, background): “Although flexible, these require multiview [57, 37, 14], one or more depth cameras [19, 45], or require the subject to stand still while turning the cameras around them [53, 38, 63, 16].”
Space-time Neural Irradiance Fields for Free-Viewpoint Video (2020, methodology): “Consequently, most of the existing methods typical require laborious multi-camera setup [78, 11, 52, 13, 6], special hardware [1], or synchronous video captures from multiple viewpoints [3, 4].”
Non-Rigid Neural Radiance Fields: Reconstruction and Novel View Synthesis of a Dynamic Scene From Monocular Video (2020, background): “More recently, the combination of multi-view stereo with fusion algorithms integrating implicit geometry over short time windows lead to improved results and short-term temporal coherence [10, 55, 22].”
A research agenda for augmented and virtual reality in architecture, engineering and construction (2020, background): “For example, some preliminary research has been carried out in this area in which a drone and a VR system are combined to scan a 100 m 2 environment [115] and dynamic mapping of furniture and people using depth cameras [127].”