Retargetting motion to new characters
Proceedings of the 25th annual conference on Computer graphics and interactive techniques
Compression of motion capture databases
ACM SIGGRAPH 2006 Papers
Segment-based human motion compression
Proceedings of the 2006 ACM SIGGRAPH/Eurographics symposium on Computer animation
Automatic knot adjustment using an artificial immune system for B-spline curve approximation
Information Sciences: an International Journal
Efficient particle swarm optimization approach for data fitting with free knot B-splines
Computer-Aided Design
Bilinear spatiotemporal basis models
ACM Transactions on Graphics (TOG)
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This paper presents a new method to approximate animation sequences through a nonlinear analysis of the spatiotemporal data. The main idea is to find a spline curve which best approximates a multivariate animation sequence in a reduced subspace. Our method first eliminates data redundancy among multiple animation channels using principal component analysis (PCA). The reduced sequence of latent variables is then approximated by a nonuniform spline with free knots. To solve the highly-nonlinear multimodal problem of the knot optimization, we introduce a stochastic algorithm called covariance matrix adaptation evolution strategy (CMA-ES). Our method optimizes the control points and the free knots using least-square method and CMA-ES, which guarantees the best approximation for arbitrary animation sequences such as mesh animations and motion capture data. Moreover, our method is applicable to practical production pipeline because both PCA-and CMA-based algorithms are computationally stable, efficient, and quasi manual parameter-free. We demonstrate the capability of the proposed method through comparative experiments with a common approximation technique.