Motion capture Applications Systems Motion capture pipeline - - PowerPoint PPT Presentation

Motion capture

• Applications
• Systems
• Motion capture pipeline
• Biomechanical analysis

Applications

Biomechanics Computer animation Robotics Video games Anthropology Cinema

What is captured?

Animals Humans Celebrities Objects

What is captured?

Whole body Face Hands

Pros and cons

• Truthfully record all the fine details of the

natural motion

• The captured motion is difficult to be
• generalized
• modified
• controlled

How to use the data?

• Off-line
• Motion libraries
• Motion graphs
• Training examples

How to use the data?

• Off-line
• Motion libraries
• Motion graphs
• Training examples
• On-line
• Drive characters based on the movement of the

actors in real time

• Applications
• Systems
• Motion capture pipeline
• Biomechanical analysis

Types of Systems

• Optical systems
• Magnetic systems
• Motion tapes
• Vision-based systems
• Inertial and ultrasonic systems

Optical systems

• Cameras
• High temporal resolution (120+ fps)
• Detect the locations of reflective markers
• Markers
• passive: sensitive to infrared
• active: emit LED light

Magnetic system

• Cumbersome sensors (heavier and also

wired)

• Smaller workspace
• Record both position and orientation
• Lower resolution (80 fps max)
• Sensitive to EMI/metal in the environment

Motion tapes

Contain optical fibers and sensors that can detect the bending and twisting Restriction of movement Need another technology for detecting root translation Measure the shape of surface precisely

Markerless mocap

• http://www.organicmotion.com/
• Kinect

Ultrasonic + Inertial

• A wearable self-contained system
• Inertial information is provided by

gyroscopes and accelerometers

• Microphones are used to record the

distance between each pair of sensors

Body-mounted cameras

• Applications
• Systems
• Motion capture pipeline
• Biomechanical analysis

Motion capture pipeline

calibration capturing model building marker labeling inverse kinematics trajectory smoothing

Motion capture pipeline

calibration capturing model building marker labeling inverse kinematics trajectory smoothing

Calibration

• Static calibration
• Figure out where the

floor is

• Dynamic calibration
• Figure out the capture

volume

Motion capture pipeline

calibration capturing model building marker labeling inverse kinematics trajectory smoothing

Capturing

• Marker placement
• Markers should move rigidly

with joints

• Asymmetric placement helps

in post-processing

• T-pose and range of motion
• Recording specific poses can

help estimating bone lengths

3D marker position

• In principle, two cameras are sufficient to

reconstruct the 3D location of a marker

• In practice, more cameras can
• reduce occlusion
• increase precision

Motion capture pipeline

calibration capturing model building marker labeling inverse kinematics trajectory smoothing

Model building

• Given recored marker positions, estimate the

dimension of each body part

• Optimize both bone length and handle positions

at the same time

• Templates and heuristics help

Problem statement

generic skeleton rough handle positions specific pose used for calibration bone length handle offset

+

Motion capture pipeline

calibration capturing model building marker labeling inverse kinematics trajectory smoothing

Marker labeling

• Ghost markers
• Missing markers
• Switching trajectories

Raw data

3D locations of markers

Motion capture pipeline

calibration capturing model building marker labeling inverse kinematics trajectory smoothing

Inverse kinematics

• Input: articulated body with handles + desired

handle positions

• Joint angles that move handles to desired

positions

Motion capture pipeline

calibration capturing model building marker labeling inverse kinematics trajectory smoothing

Trajectory smoothing

• Global optimization that minimizes the velocity
• f the joint angles while staying as close as

possible to the desired handle positions

Final motion

Issues

The main problem with motion capture associated with characters has to do with mass distribution, weight and exaggeration. It is impossible for a performer to produce the kind of motion exaggeration that a cartoon character needs, and the mass and weight of the performer almost never looks good when applied to a character

• f different proportions.

Eric Darnell, codirector of Antz

Issues

The mapping of human motion to a character with non-human proportions doesn’t work, because the most important things you get out

• f motion capture are the weight shifts and the

subtleties and that balancing act of the human body. If the proportions change, you throw all that out the door, so you might as well animate it.

Richard Chuang, VP at PDI