Animating rotation with quaternion curves
SIGGRAPH '85 Proceedings of the 12th annual conference on Computer graphics and interactive techniques
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Understanding Motion Capture for Computer Animation and Video Games
Understanding Motion Capture for Computer Animation and Video Games
Motion texture: a two-level statistical model for character motion synthesis
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Practical parameterization of rotations using the exponential map
Journal of Graphics Tools
Realistic synthesis of novel human movements from a database of motion capture examples
HUMO '00 Proceedings of the Workshop on Human Motion (HUMO'00)
PCA-Based Walking Engine Using Motion Capture Data
CGI '04 Proceedings of the Computer Graphics International
An efficient search algorithm for motion data using weighted PCA
Proceedings of the 2005 ACM SIGGRAPH/Eurographics symposium on Computer animation
Computational studies of human motion: part 1, tracking and motion synthesis
Foundations and Trends® in Computer Graphics and Vision
Exploiting Quaternion PCA in Virtual Character Motion Analysis
ICCVG 2008 Proceedings of the International Conference on Computer Vision and Graphics: Revised Papers
Reuse of motion capture data in animation: a review
ICCSA'03 Proceedings of the 2003 international conference on Computational science and its applications: PartIII
Automatic 3d motion synthesis with time-striding hidden markov model
ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
On Learning, Representing, and Generalizing a Task in a Humanoid Robot
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In this paper we analyze walking sequences of an actor performing walk under eleven different states of mind. These walk sequences captured with an inertial motion capture system are used as training data to model walk in a reduced dimension space through principal component analysis (PCA). In that reduced PC space, the variability of walk cycles for each emotion and the length of each cycle are modeled using Gaussian distributions. Using this modeling, new sequences of walk can be synthesized for each expression, taking into account the variability of walk cycles over time in a continuous sequence.