1. Notice
• This power point is made by Mitsuru
Nakazawa, NOT an original author, for paper
introduction of ECCV2012
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2. Presenter: Mitsuru NAKAZAWA
Performance Capture of Interacting
Characters with Handheld Kinects
Genzhi Ye1 Yebin Liu1 Nils Hasler2
Xiangyang Ji1 Qionghai Dai1 Christian Theobalt2
1: Deptartment of Automation, Tsinghua University
2: Graphics, Vision & Video Group, Max-Planck Institute for Informatics
ECCV2012 paper introduction 2

3. Introduction movie
URL: http://media.au.tsinghua.edu.cn/yegenzhi/HandheldKinectsMocap_ECCV2012.jsp
(Accessed on 26th Nov. 2012)
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4. Related works
Multi-view motion capture approaches
 Reconstruct a skeletal motion model & detailed
dynamic surface geometry
 Deal with people wide apparel
 Require controlled studio setup (many number
of sync video cameras)
Marker-less motion capture from a single range
sensor
 Estimate complex poses at real-time frame rates
 Difficult to capture 3D, complex, detailed model
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5. Objective
freely move
Full performance capture Operator
of moving humans using
only 3 handheld,
Performer
moving Kinects
 Reconstruct a skeleton motion & time varying surface
geometry of humans in general apparel
 Handle fast and complex motion with many self-
occlusions & non-rid surface deformation
 Not need studios with controlled lighting and many
stationary cameras
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6. Data capture
Operator
Performer
Capture environment Captured data from 3 Kinects
 Asynchronous capture
 Use a start recording signal to all PCs connected through Wi-Fi
 Intrinsic calibration
 Apply Zhang’s method
 Alignment between the color image and the range data
 Use the OpenNI API
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7. Scene models at time t
• Human model
– Laser scanner provide a static mesh with embedded
skeleton of each performer
[*]
• 5,000 vertices of meshes
• k-th performer’s Skeleton with
31 degrees of freedom: C tk
GND (r=3m)
• Ground plane model (fixed)
– Center of Environment
– Planar mesh with circular boundary
• Camera extrinsic parameters of i-th Kinect
– Translation, rotation: L tk
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[*] F. Remondino: “3-D reconstruction of static human body shape from image sequence,” CVIU, Vol.93, No.1. pp.65-85

8. Overview of the proposed method
at time
t Geometric matching of Kinect point to
Point Could Segmentation
vertices of a human model
Optimization of skeleton & camera pose
Non-rigid deformation of the human
surface via Laplacian deformation
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9. Optimization of skeleton and camera pose
Error function E is solved within iterative quasi-Newton minimization
• Human region error between model vertices and Kinect point cloud
• Ground region error between model vertices and Kinect point cloud
• Difference of matched SIFT feature positions between previous and current
time on background regions (SfM approach)
Result using t−1 & t−1 Result based on SfM approach Optimized result 9

10. Comparison with Multi-view Video Tracking
Multi-view video trachking system with 10 calibrated cameras vs. Proposed method
“Rolling” with slow motion  Similar results
“Jump” with fast motion  proposed method gets better results
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11. Performance capture results
on a variety of Sequences
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12. Conclusion
• Simultaneously marker-less performance
capture system with several hand-held Kinects
– Iterative robust matching of tracked 3D models
and input Kinect data
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13. References
• Linear Blend Skinning (Accessed on Nov. 25th 2012 )
– http://bit.ly/RaijkQ
• Motion Capture Using Joint Skeleton Tracking and
Surface Estimation (Accessed on Nov. 26th 2012)
– http://www.vision.ee.ethz.ch/~gallju/projects/skelsurf/ind
ex.html
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