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SEMINAR PRESENTATION
Coordinated by,
Anupama A S
Asst Professor
Dept of ECE,
SIST
Presented by,
Arunima Unnikrishnan
S7 EC(13133414)
PUPPETOOONER
Guided by,
Renjith Sudarshanan
Asst Professor
Dept of ECE,
SIST
 Abstract
 Introduction
 Technologies & principles
 Kinect depth sensor
 Motion capture
 Capture module
 Adavantages
 Disadvantages
 Applications
 Future scope
 Conclusion
 References
 Thank you
 3D animation using miniature model.
 Puppets manipulated directly infront of kinect
depth sensor.
 Motion capturing and projection mapping used.
 Operates in real time.
 No prior animation experience needed.
 3D animations using physical objects.
 Usually 3D animation done by software.
 Software deals with complex tools
&commands.
 Increases processing time.
 Direct manipulation of puppets during
performance.
 Kinect depth sensor is used.
 Relation between real and virtual spaces.
 Image feature matching &3D shape matching.
 Puppets operated physically.
 Kinect depth sensors used.
 Tangible interfaces.
 Object based motion capture.
 Projection mapping.
 Video puppetry.
 Tracks human body and provides interaction
with virtual objects.
 Uses infrared laser light with a speckle pattern.
 First compute a depth
map, then infer body
position
Kinect depth sensor
 Consist of IR camera and laser based IR
projector.
 IR projector projects known pattern read by IR
camera.
 Physical puppet 3d model.
 Scanning and moving round the puppets.
 Stream of kinect cloud untextured 3d mesh.
 Single kinects and multi kinects.
Painting mode for colouring.
Physical puppet ReconstructMe
model
Colorized model
 Sampling and recording motion of objects as 3d data
 Object-based motion-capture systems.
 A single Kinect and a table laptop.
 Filter out points belonging to the puppeteer’s hands.
 Puppets with colors similar to puppeteer’s skin color
not used.
 Mainly 3 components.
 Puppet Identifier.
 Point-cloud segmenter.
 Pose Tracker.
 Inputs to Capture module-Puppet Database and Kinect
data.
 Puppet Identifier Point-cloud Segmenter Pose Tracker.
 PUPPET IDENTIFIER
 Identifies puppets within Kinect field.
 Estimates poses initialization for Pose Tracker.
 Find scale-invariant feature transform (SIFT).
 POINT CLOUD SEGMENTER
 Removes background points & hands from cloud.
 Splits remaining points to separate clouds.
Raw point cloud Background Removed Skin Removed Segmented by
Puppet
 POSE TRACKER
 Estimates pose of puppet accurately.
 Aligns segmented cloud points with 3d model database.
 Use iterative closest point (ICP) algorithm.
Initial Alignment 2 Iterations 4 Iterations 32 Iterations
Capture module
 PROJECTION MAPPING
 Video mapping and spatial augmented reality.
 Irregularly shaped objects display surface for
video projection.
 Texture,events&animations of 3d model projected to
miniature models.
 3D characters controlled physically.
 No complex tools or command set.
 Quick 3d animation.
 Operates in real time.
 Inexpensive capture hardware&simple setup.
 Familiar and approachable.
 Natural looking motion.
 Layered animation used.
 Flexibility and accessability.
 Enjoyable user experience.
 Deformable puppets not used.
 Mismatches can occur.
 Tracking errors.
 Gaming.
 Cartoons.
 Integrated Puppet Loading.
 Articulated Puppets.
 Deformable Puppets.
 Multiple Puppeteers.
 New interface for 3d animation with physical objects.
 Puppeteers focus on physical performance.
 User friendly and enjoyable technique.
 3d animation in hands.
 IEEE explorer official website-2013 paper.
 Besl, P., and McKay, N. A method for registration of 3-
D shapes. IEEE PAMI 14, 239–256.
 1. Autodesk. 123D Catch.
http://www.123dapp.com/catch.
 2. Autodesk. 3ds Max. http://usa.autodesk.com/3ds-
max/.
 3. Autodesk. Maya. http://usa.autodesk.com/maya/.
 4. Avrahami, D., Wobbrock, J. O., and Izadi, S.
Portico:tangible interaction on and around a tablet. In
Proc.UIST (2011), 347–356.
QUESTIONS
……???
Aru pup

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Aru pup

  • 1.
  • 3. Coordinated by, Anupama A S Asst Professor Dept of ECE, SIST Presented by, Arunima Unnikrishnan S7 EC(13133414) PUPPETOOONER Guided by, Renjith Sudarshanan Asst Professor Dept of ECE, SIST
  • 4.  Abstract  Introduction  Technologies & principles  Kinect depth sensor  Motion capture  Capture module  Adavantages  Disadvantages  Applications  Future scope  Conclusion  References  Thank you
  • 5.  3D animation using miniature model.  Puppets manipulated directly infront of kinect depth sensor.  Motion capturing and projection mapping used.  Operates in real time.  No prior animation experience needed.
  • 6.  3D animations using physical objects.  Usually 3D animation done by software.  Software deals with complex tools &commands.  Increases processing time.  Direct manipulation of puppets during performance.  Kinect depth sensor is used.  Relation between real and virtual spaces.
  • 7.  Image feature matching &3D shape matching.
  • 8.  Puppets operated physically.  Kinect depth sensors used.  Tangible interfaces.  Object based motion capture.  Projection mapping.  Video puppetry.
  • 9.  Tracks human body and provides interaction with virtual objects.  Uses infrared laser light with a speckle pattern.  First compute a depth map, then infer body position
  • 10.
  • 12.  Consist of IR camera and laser based IR projector.  IR projector projects known pattern read by IR camera.  Physical puppet 3d model.  Scanning and moving round the puppets.  Stream of kinect cloud untextured 3d mesh.  Single kinects and multi kinects.
  • 13. Painting mode for colouring. Physical puppet ReconstructMe model Colorized model
  • 14.  Sampling and recording motion of objects as 3d data  Object-based motion-capture systems.  A single Kinect and a table laptop.  Filter out points belonging to the puppeteer’s hands.  Puppets with colors similar to puppeteer’s skin color not used.
  • 15.  Mainly 3 components.  Puppet Identifier.  Point-cloud segmenter.  Pose Tracker.  Inputs to Capture module-Puppet Database and Kinect data.  Puppet Identifier Point-cloud Segmenter Pose Tracker.
  • 16.  PUPPET IDENTIFIER  Identifies puppets within Kinect field.  Estimates poses initialization for Pose Tracker.  Find scale-invariant feature transform (SIFT).  POINT CLOUD SEGMENTER  Removes background points & hands from cloud.  Splits remaining points to separate clouds.
  • 17. Raw point cloud Background Removed Skin Removed Segmented by Puppet
  • 18.  POSE TRACKER  Estimates pose of puppet accurately.  Aligns segmented cloud points with 3d model database.  Use iterative closest point (ICP) algorithm. Initial Alignment 2 Iterations 4 Iterations 32 Iterations
  • 20.  PROJECTION MAPPING  Video mapping and spatial augmented reality.  Irregularly shaped objects display surface for video projection.  Texture,events&animations of 3d model projected to miniature models.
  • 21.  3D characters controlled physically.  No complex tools or command set.  Quick 3d animation.  Operates in real time.  Inexpensive capture hardware&simple setup.
  • 22.  Familiar and approachable.  Natural looking motion.  Layered animation used.  Flexibility and accessability.  Enjoyable user experience.
  • 23.  Deformable puppets not used.  Mismatches can occur.  Tracking errors.
  • 25.
  • 26.  Integrated Puppet Loading.  Articulated Puppets.  Deformable Puppets.  Multiple Puppeteers.
  • 27.  New interface for 3d animation with physical objects.  Puppeteers focus on physical performance.  User friendly and enjoyable technique.  3d animation in hands.
  • 28.
  • 29.  IEEE explorer official website-2013 paper.  Besl, P., and McKay, N. A method for registration of 3- D shapes. IEEE PAMI 14, 239–256.  1. Autodesk. 123D Catch. http://www.123dapp.com/catch.  2. Autodesk. 3ds Max. http://usa.autodesk.com/3ds- max/.  3. Autodesk. Maya. http://usa.autodesk.com/maya/.  4. Avrahami, D., Wobbrock, J. O., and Izadi, S. Portico:tangible interaction on and around a tablet. In Proc.UIST (2011), 347–356.