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Advanced Computer Graphics (Fall 2010) CS 283, Lecture 24: Motion Capture Ravi Ramamoorthi http://inst.eecs.berkeley.edu/~cs283/fa10 Most slides courtesy James O’Brien from CS294-13 Fall 2009
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Motion Capture Record motion from physical actors Use motion to animate virtual objects Retarget and optimize as needed General trend of data-driven appearance (BRDF)
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Captures “Signature” of Actor
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Types of Objects and Motions Human, whole body Portions of body Facial animation Animals Puppets and cartoons …
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Capture Equipment
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Types of capture equipment
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Active Optical
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Facial MoCap
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Auto Calibration
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Parameter Estimation
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Manipulating Motion Data WYSIWYG vs WYSIAYG Basic Tasks Adjusting Blending Transitioning Retargeting Building graphs
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Nature of Motion Data
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Adjusting
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Adjustment Define desired motion function in parts Select adjustment function from nice space, such as C2 B-splines Spread modification over reasonable time period User selects support radius
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Adjusting
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Blending Given two motions make a motion that combines qualities of both Assume same DOFs Assume same parameter mappings
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Blending Blending slow walk and fast walk
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Time Warping Define timewarp functions to align features
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Blending in Time Blend in normalized time Blend playback rate
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Blending and Contacts Blending may still break features in original motion
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Blending Add explicit constraints to key points Enforce with IK over time
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Blending / Adjustments
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Multivariate Blending Extend blending to multivariate interpolation Scattered data interpolation methods as needed
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Transitions Transition from one motion to another
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Cyclification Special case of transitioning Both motions are the same Need to modify beginning and end simultaneously
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Motion Graphs Hand built motion graphs often used in games Significant amount of work required Limited number of transitions by design Motion graphs can also be built automatically
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Motion Graphs Similarity Metric Measurement of how similar two frames of motion are Based on joint angles or point positions Must include some measure of velocity Ideally independent of capture setup and skeleton Capture a “large” database of motions Compute similarity between all pairs of frames Can be expensive, but preprocessing step May be many good edges
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Motion Graphs Random Walks Start in some part of the graph, randomly make transitions Avoid dead ends Useful for “idling” behaviors Transitions Use blending algorithm we discussed
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Motion Graphs Can have requirements Start at particular location, End at particular Pass through some points Can be solved using dynamic programming Efficiency may require approximate solution Notion of goodness of a solution
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Reordering
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Content Tags
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Integrating Physics Simulation added to base motion Inverse dynamics for matching Oracle to assess results
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Integrating Physics
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Suggested Reading 1
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Suggested Reading 2
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