Algorithms for clustering data
Algorithms for clustering data
Proceedings of the Eurographics workshop on Computer animation and simulation '96
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Learning variable-length Markov models of behavior
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Interactive motion generation from examples
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
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)
Style-based inverse kinematics
ACM SIGGRAPH 2004 Papers
Hierarchical implicit surface joint limits for human body tracking
Computer Vision and Image Understanding
A sketch-based articulated figure animation tool
Proceedings of the South African Institute of Computer Scientists and Information Technologists Conference on Knowledge, Innovation and Leadership in a Diverse, Multidisciplinary Environment
Modeling joint synergies to synthesize realistic movements
GW'09 Proceedings of the 8th international conference on Gesture in Embodied Communication and Human-Computer Interaction
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Existing work on animation synthesis can be roughly split into two approaches, those that combine segments of motion-capture data, and those that perform inverse kinematics. In this paper, we present a method for performing animation synthesis of an articulated object (e.g. human body and a dog) from a minimal set of body joint positions, following the approach of inverse kinematics. We tackle this problem from a learning perspective. Firstly, we address the need for knowledge on the physical constraints of the articulated body, so as to avoid the generation of a physically impossible poses. A common solution is to heuristically specify the kinematic constraints for the skeleton model. In this paper however, the physical constraints of the articulated body are represented using a hierarchical cluster model learnt from a motion capture database. Additionally, we shall show that the learnt model automatically captures the correlation between different joints through simultaneous modelling of their angles. We then show how this model can be utilised to perform inverse kinematics in a simple and efficient manner. Crucially, we describe how IK is carried out from a minimal set of end-effector positions. Following this, we show how this "learnt inverse kinematics" framework can be used to perform animation syntheses on different types of articulated structures. To this end, the results presented include the retargeting of a flat surface walking animation to various uneven terrains to demonstrate the synthesis of a full human body motion from the positions of only the hands, feet and torso. Additionally, we show how the same method can be applied to the animation synthesis of a dog using only its feet and torso positions.