Dynamic cell structure learns perfectly topology preserving map
Neural Computation
Retargetting motion to new characters
Proceedings of the 25th annual conference on Computer graphics and interactive techniques
Stochastic Tracking of 3D Human Figures Using 2D Image Motion
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Generative modeling for continuous non-linearly embedded visual inference
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Style-based inverse kinematics
ACM SIGGRAPH 2004 Papers
Priors for People Tracking from Small Training Sets
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
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We propose a model for learning the articulated motion of human arm. The goal is to generate plausible trajectories of joints that mimic the human movement using deformation information. The trajectories are then mapped to constraint space. These constraints can be the space of start and end configuration of the human body and task-specific constraints such as avoiding an obstacle, picking up and putting down objects. This movement generalization is a step forward from existing systems that can learn single gestures only. Such a model can be used to develop humanoid robots that move in a human-like way in reaction to diverse changes in their environment. The model proposed to accomplish this uses a combination of principal component analysis (PCA) and a special type of a topological map called the dynamic cell structure (DCS) network. Experiments on a kinematic chain of 2 joints show that this model is able to successfully generalize movement using a few training samples for both free movement and obstacle avoidance