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A Hierarchical Approach toMotion Analysis and Synthesisfor Articulated FiguresJehee Lee
Computer Science DepartmentKAIST
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Content1. Introduction2. Spatial filtering for orientation data3. Motion editing with spacetime constraints4. Multiresolution motion analysis and synthesis5. Conclusion and Future work
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Character AnimationRealistic motion datamotion capture technologycommercial librariesProduce animation from available motion clipsrequires specialized toolsinteractive editing, smoothing, enhancement, blending, provides a great variety incharacter size, environment geometry, scenario, ...
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Motion of Articulated FiguresStructure of motion databundle of motion signalseach signal represents either position or orientationDifficulties in handling motion dataorientations yield complicationsunit quaternionsmutual relationships among motion signals
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OverviewDesigning spatial filters for motion dataMotion editing with spacetime constraintsMultiresolution motion analysis and synthesisEditing &AdaptationAnalysis &SynthesisFilteringrawinputsignalconvincinganimation ofarbitrary lengthCrafting animation from motion-captured data
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Content1. Introduction2. Spatial filtering for orientation data3. Motion editing with spacetime constraints4. Multiresolution motion analysis and synthesis5. Conclusion and Future work
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Spatial Filtering for Orientation DataLinear shift-invariant (LSI) filterfilter mask : vector-valued signal :
Not suitable for unit quaternion dataunit-length constraints
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Previous WorkBrute-force normalizationAzuma and Bishop (94)Employ exponential and logarithmic mapsLee and Shin (96)Fang et al (98)Hsieh et al (98)lack of crucial filter properties
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RequirementsAvoid brute-force normalizationSatisfy desired propertiesIndependent of coordinate systemIndependent of timeIndependent of reflection
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Basic IdeaExploit correspondence in differential spacesLinear motion : Angular motion : VelocityAcceleration
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TransformationTransformation between linear and angular signals
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Filter DesignGiven: spatial filter FOutput: spatial filter H for orientation data
filter responses of unit lengthlocally supported#support(H ) = #support(F )
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Filter DesignGiven: spatial filter FOutput: spatial filter H for orientation data
filter responses of unit lengthlocally supported#support(H ) = #support(F )
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Properties of Orientation FiltersCoordinate-invariance
Shift-invariance
Symmetry
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Experimental Results (1)Blurring by binomial masksOriginalAngular accelerationFilteredOriginalFiltered
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Experimental Results (2)SmoothingOriginalAngular accelerationFilteredOriginalFiltered
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Experimental Results (3)High-frequency boostingOriginalAngular accelerationFilteredOriginalFiltered
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VideoSmoothing and Sharpening
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Summary (Motion Filtering)A general scheme of constructing spatial filters for orientation datasatisfies desired propertiessimple, efficient, and easy to implementperforms well for live-captured data
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Content1. Introduction2. Spatial filtering for orientation data3. Motion editing with spacetime constraints4. Multiresolution motion analysis and synthesis5. Conclusion and Future work
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Motion EditingReuse available motion clipsInteractive motion editingdirect manipulation through graphical interfaceMotion adaptationnew charactersnew environmentsnew scenario
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Spacetime FormulationSpacetime constraints[Witkin and Kass 88] [Cohen 92] [Gleicher 98]important features of the original motionnew features to be accomplishedTo find a new motionsatisfying given constraintspreserving original characteristics
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Previous WorkGeometric techniquesBruderlin and Williams (95)Popovic and Witkin (95)lack of consideration on kinematic constraintsSpacetime optimizationRose et al (96)Gleicher (98)yield very large optimization that is cumbersome to handle
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VideoDirect manipulation with spacetime constraints
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Basic IdeaStructure of motion sequencesIntra-frame relationshipsatisfying constraintsby inverse kinematicsInter-frame relationshipavoiding jerkinessby curve fitting
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Motion RepresentationConfiguration of articulated figurescomprises both linear and angular components
rigid transformation
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Motion Displacement Mapping
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Adaptive RefinementFlexibility in representationspline curves over a uniform knot sequence hard to determine knot densityadaptive refinement is neededMultilevel or hierarchical B-splines[Lee, Wolberg and Shin 97] [Forsey and Bartels 95] sum of uniform B-spline functionscoarse-to-fine hierarchy of knot sequences
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Multilevel B-spline FittingSmooth initial approximation
Chart4
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Multilevel B-spline FittingRefinementwith finer functions
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