Motion Capture Recent Trends Rama Hoetzlein 2011 Lecture

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

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

Facial Capture 1988 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

Trend – Markerless Facial Capture Surface is tracked based on image distortion rather than markers. Emily, Image Metrics, 2010 http: //www. youtube. com/watch? v=UYg. LFt 5 wf. P 4&feature=player_embedded

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?

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.

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

Hardware Trends

Trend – Markerless capture: Origins in 3 D laser scanning 3 D Lego Digitizer http: //www. rchoetzlein. com/project/digitizer/

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.

Point cloud Volume construction 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. Fit torse Fit extremities

Trend – Markerless capture: Direct-to-3 D models Performance Capture from Sparse Multi-view Video, SIGGRAPH 2010 Christian Theobalt, Stanford University http: //www. youtube. com/watch? v=d. Tis. U 4 dib. Sc&playnext=1& list=PLD 31 C 3 C 36 D 294 EEDB http: //www. stanford. edu/group/biomotion/Markerless. html

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 3 D Reconstruction of Human Skeleton from Single Images or Monocular Video Sequences 2003, 25 th Pattern Recognition Symposium

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

Software Trends

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

Trend – Motion Graphs in Gaming Planning and Directing Motion Capture For Games Melianthe Kines, Gamasutra. January 19, 2000 http: //www. gamasutra. com/view/feature/3420/planning_and_directing_motion_. php

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

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 3 D Graphics 2003

Common solutions in Gaming: 1. Jump transitions Linear blending between motion clips 2. Motion may not match scene exactly Blend with scene constraints (extend jump over river) 3. Cannot grasp objects Add inverse kinematics to arms in game characters. 4. Cannot move in any direction Add steering. Simple: re-orient, then play walk cycle Advanced: add IK to legs 5. Interruptions from outside forces Use a rag-doll physics switch. When object hits. . Turn on physics, apply force.

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.

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

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