Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
The use of positional information in the modeling of plants
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Mapping optical motion capture data to skeletal motion using a physical model
Proceedings of the 2003 ACM SIGGRAPH/Eurographics symposium on Computer animation
Volumetric reconstruction and interactive rendering of trees from photographs
ACM SIGGRAPH 2004 Papers
Skeletal Parameter Estimation from Optical Motion Capture Data
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Animating pictures with stochastic motion textures
ACM SIGGRAPH 2005 Papers
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
Hierarchical retargetting of 2D motion fields to the animation of 3D plant models
Proceedings of the 2006 ACM SIGGRAPH/Eurographics symposium on Computer animation
ACM SIGGRAPH 2007 papers
Approximate image-based tree-modeling using particle flows
ACM SIGGRAPH 2007 papers
Quasi-physical simulation of large-scale dynamic forest scenes
CGI'06 Proceedings of the 24th international conference on Advances in Computer Graphics
A system for marker-less human motion estimation
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
Reconstructing 3D tree models using motion capture and particle flow
International Journal of Computer Games Technology
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Simulating the motion of a tree in the wind is a difficult problem because of the complexity of the tree's geometry and its associated wind dynamics. Physically-based animation of trees in the wind is computationally expensive, while noise-based approaches ignore important global effects, such as sheltering. Motion capture may help solve these problems. In this paper, we present new approaches to inferring a skeleton from tree motion data and repairing motion data using a rigid body model. While the rigid body model can be used to extract data, the data contains many gaps and errors for branches that bend. Motion data repair is critical because trees are not rigid bodies. These ideas allow the reconstruction of tree motion including global effects but without a complex physical model.