Robust regression and outlier detection
Robust regression and outlier detection
Footskate cleanup for motion capture editing
Proceedings of the 2002 ACM SIGGRAPH/Eurographics symposium on Computer animation
Synthesis of complex dynamic character motion from simple animations
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Interactive control of avatars animated with human motion data
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Interactive motion deformation with prioritized constraints
SCA '04 Proceedings of the 2004 ACM SIGGRAPH/Eurographics symposium on Computer animation
Synchronization for dynamic blending of motions
SCA '04 Proceedings of the 2004 ACM SIGGRAPH/Eurographics symposium on Computer animation
Knowing when to put your foot down
I3D '06 Proceedings of the 2006 symposium on Interactive 3D graphics and games
Quick transitions with cached multi-way blends
Proceedings of the 2007 symposium on Interactive 3D graphics and games
Construction and optimal search of interpolated motion graphs
ACM SIGGRAPH 2007 papers
Achieving good connectivity in motion graphs
Graphical Models
Achieving good connectivity in motion graphs
Proceedings of the 2008 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Proceedings of the 2008 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Perceptual evaluation of footskate cleanup
SCA '11 Proceedings of the 2011 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Evaluating the distinctiveness and attractiveness of human motions on realistic virtual bodies
ACM Transactions on Graphics (TOG)
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Motion capture data is now widely available to create realistic character animation. However, it is difficult to reuse without any additional information. For this reason, annotating motion data with kinematic constraints is a clever step to ease further operations such as blending or motion editing. Unfortunately, prior automatic methods prove to be unreliable for noisy data and/or lack genericity. In this paper, we present a method for detecting kinematic constraints for motion data. It detects when an object (or an end-effector) is stationary in space or is rotating around an axis or a point. Our method is fast, generic and may be used on any kind of objects in the scene. Furthermore, it is robust to highly noisy data as we detect and reject aberrant data by using a least median of squares (LMedS) method. We demonstrate the accuracy of our method in various motion editing contexts.