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demo . Personal Telepresence Station. Zhengyou Zhang Principal Researcher Microsoft Research. Telepresence: What. A videoconferencing experience that creates the illusion that the remote participants are in the same room with you. . - Wainhouse Research 2007. Cisco Telepresence $299k .

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personal telepresence station

Personal Telepresence Station

Zhengyou Zhang

Principal Researcher

Microsoft Research

telepresence what
Telepresence: What
  • A videoconferencing experience that creates the illusion that the remote participants are in the same room with you.

- Wainhouse Research 2007

Cisco Telepresence $299k

Tandberg Experia $225k

HP Halo $425k + $18k/mo

Polycom RPX210M $269k + $18.5k/mo

telepresence why
Telepresence: Why
  • Economic globalization and workforce mobilization
  • 7-38-55% rule for communicating feelings and altitudes
    • 7%: Verbal (Words, What you say)
    • 38%: Vocal (Tone of voice, How you say them)
    • 55%: Visual (Gaze, Facial expression, Body language)

Customer Pains

  • Product cycles dependent on remote teams
  • Increased travel time and costs
  • Energy consumption, CO2 production

Bandwidth & Hardware Costs

microsoft personal telepresence station
Microsoft Personal Telepresence Station
  • Replicate experience people enjoy in face-to-face meeting
    • Dedicated camera-display-speaker for remote stand-in
    • Tailored to a single person  Correct spatial cues
  • Prototypes

Full Mesh Geometry

still many challenges
Still Many Challenges
  • Make it more immersive?
  • Design user interface for both audio-visual and data?
  • Adapt to network conditions?
  • Combine HW & SW for better capture and rendering?
audio spatialization
Audio Spatialization
  • Provide virtual seating
  • Improve understandingby remote participants
  • Decrease cognitive load (cocktail party effect)




sorry, can’t demo in this room…

active lighting
Active Lighting
  • Computer-controllable lighting system
    • Automatically adjust light intensitybased on video analysis
  • Skin tone model
    • learned from hundreds of celebrity images
  • Optimization of face image quality
    • with physical parameters in the loop

Zicheng Liu

Rajesh Hegde

Qin Cai

Cha Zhang

Sasa Junuzovic

Christian Huitema

Jayman Dalal

Wanghong Yuan

© 2008 Microsoft Corporation. All rights reserved. Microsoft, Windows, Windows Vista and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.