1. Motion Capture: Recent Trends
Rama Hoetzlein, 2011 Lecture Notes Aalborg University at Copenhagen

2. Facial Capture
Frederick I. Parke, University of Utah Computer Generated Animation of Faces 1972

3. Facial Capture
B. Robertson, Mike the Talking Head, Computer Graphics World 11 (7):57
Method 1 – Phonemes When a particular type of sound is spoken: phonemes Specific shapes of the whole face are captured. (top down) Phonemes – the sounds that make up a word, not letters balloon b – ah – l – oo – n
Method 2 – Feature Tracking Parts of the face are tracked separately. Each part contributes to overall motion. (bottom up) Motion is the sum of many features. Works for speech and other facial expressions

4. Trend – Markerless Facial Capture
Surface is tracked based on image distortion rather than markers.
Emily, Image Metrics, 2010

5. Problem: Motion capture records the body over volumes up to:
Problem: Motion capture records the body over volumes up to: 10 x 10 sq. meters (30 sq. ft) Facial capture records subtle details over space of: 30 x 30 cm (1 sq. ft)
How to capture both the large-scale motion of the body
and subtle motion of the face during a single performance?

6. Trend – Performance capture is a collection of
techniques that combine to record the total motion of an actor.
Avater (2010), James Cameron
Solution: Block off the face using individual, head-mounted cameras, which record only the face. Use motion cameras and passive markers for the body. Allows for both large volumes and small details.

7. Markers include: Body capture Green lines, white dots Facial capture Head-mounted device, /w camera booms Hair capture Blue and red ropes

8. Hardware Trends

9. Trend – Markerless capture: Origins in 3D laser scanning
3D Lego Digitizer

10. Trend – Markerless capture: Structured Light
Q: High frequency gives details about height of point. But how do we tell if the point is on left or right side of obj?
Faster: Do all lines at once Projector with structured light mapped onto the object. Use two cameras to determine object structure. Structured light can be linear, binary coded, gray coded, or color coded. The encoding allows you to uniquely identify points. Light may be infrared (Kinetic).
A: Low frequency gives overall characteristics of pixels.

11. Point cloud
Volume construction
Fit torse
Fit extremities
No markers. Structured light creates a point cloud. Skeleton is fit inside point cloud from root joints to extremities. Torso defines primary orientation, and also constraints placement of next joint layer in hierarchy.

12. Trend – Markerless capture: Direct-to-3D models
Performance Capture from Sparse Multi-view Video, SIGGRAPH 2010
Christian Theobalt, Stanford University

13. Trend – Monocular capture
One camera, without depth, is under-constrained. However, the human body has fixed limb lengths and ratios. Use the body ratios as an additional constraint.
Fabio Remondino, Andreas Roditakis
Institute for Geodesy and Photogrammetry - ETH Zurich, Switzerland
3D Reconstruction of Human Skeleton from Single Images or Monocular Video Sequences 2003, 25th Pattern Recognition Symposium

14. Trend – Low Cost Systems
Cheap hardware: Microsoft Kinect, Web cameras. Open source software: OpenKinect open kinect drivers libfreenect open kinect drivers OpenNI skeleton fitting FaceAPI facial tracking Main challenges: 1) Integration into existing frameworks, 2) Usually requires programming experience 3) Can be difficult to modify for research

15. Software Trends

16. Motion Graphs
Motion Graph: A database of motion capture clips, connected to one another to represent transitions between actions. Motion graphs can be represented by a finite state machine, a set of states with edges representing state transitions.
Stand
Run
Jump

17. Trend – Motion Graphs in Gaming
Planning and Directing Motion Capture For Games Melianthe Kines, Gamasutra. January 19,

18. What are the advantages and disadvantages of motion graphs for gaming
What are the advantages and disadvantages of motion graphs for gaming? Advantages 1. Fast. Motion is simply played back from pre-recorded data. 2. Interactive. Motion can be changed immediately by transitioning to a different state. 3. Modular. Different motions can easily be swapped in. 4. Extensible. More states can be added to the graph. Disadvantages 1. Jump transitions between capture clips 2. Motion may not match scene exactly. e.g. jump over chasm 3. Cannot grasp objects accurately. No inverse kinematics. 4. Cannot move in any direction 5. Interruptions from outside forces not easily handled

19. Trend – Motion Blending in Gaming
“In order to create streams of high-quality motion, current applications [games]
assemble static clips of motion created with traditional animation techniques such as motion capture or keyframing. The assembly process requires making transitions between motions. These
transitions may be difficult to create, such as a transition between a running clip and one where the character is lying down, or trivial, if the end of one clip is identical to the beginning of the next. In practice,
simple techniques such as linear blends are capable of creating transitions in cases where the motions are similar.”
Michael Gleicher, Hyun Joon Shin, Lucas Kovar, Andrew Jepsen
Snap-Together Motion: Assembling Run-Time Animations Interactive 3D Graphics 2003

20. Common solutions in Gaming: 1. Jump transitions
Common solutions in Gaming: 1. Jump transitions Linear blending between motion clips 2. Motion may not match Blend with scene constraints scene exactly (extend jump over river) 3. Cannot grasp objects Add inverse kinematics to arms in game characters Cannot move in any Add steering direction Simple: re-orient, then play walk cycle Advanced: add IK to legs 5. Interruptions from Use a rag-doll physics switch outside forces When object hits Turn on physics, apply force.

21. Trend – Motion Graphs
How would you instruct a character to follow an arbitrary path using a set of pre-recorded captured motion?
Lucas Kovar, Michael Gleicher, Frederic Pighin. Motion Graphs, SIGGRAPH 2002.

22. Construction and optimal search of interpolated motion graphs
Trend – Motion Graphs
How do we make energy optimal motion based on several, arbitrary constraints? Uses motion capture data, but in arbitrary, non-acted scenarios.
Alla Safonova Jessica K. Hodgins,
Carnegie-Melon University.
Construction and optimal search of interpolated motion graphs
SIGGRAPH 2007

23. INPUT OUTPUT Overview Post processing (cleaning)
Physical capture / Haptics
Joint data
Animation
Skinning
Secondary motion
Marker data
Blending Optimization
Re-targeting
Motion capture
Motion graphs (e.g Gaming)
Sequencing
Monoccular video
Skeleton fitting
Performancecapture
Point clouds
Facial capture
3D model capture