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Interactive Low-Dimensional Human Motion Synthesis by Combining Motion Models and PIK

Related publications (47)

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A learning by imitation model handling multiple constraints and motion alternatives

Aude Billard, Sylvain Calinon, Eric Sauser, Florent D'Halluin

We present a probabilistic approach to learn robust models of human motion through imitation. The association of Hidden Markov Model (HMM), Gaussian Mixture Regression (GMR) and dynamical systems allows us to extract redundancies across multiple demonstrat ...

2010

Learning and reproduction of gestures by imitation: An approach based on Hidden Markov Model and Gaussian Mixture Regression

Aude Billard, Sylvain Calinon, Eric Sauser, Florent D'Halluin

Institute of Electrical and Electronics Engineers2010

Encoder and Decoder Side Global and Local Motion Estimation for Distributed Video Coding

Touradj Ebrahimi, Frédéric Dufaux

In this paper, we propose a new Distributed Video Coding (DVC) architecture where motion estimation is performed both at the encoder and decoder, effectively combining global and local motion models. We show that the proposed approach improves significantl ...

2010

Data-driven constraint-based motion editing

The growth of motion capture systems has contributed to the proliferation of human motion database, mainly because human motion is important in many applications, ranging from games entertainment and films to sports and medicine. However, the various captu ...

EPFL2009

Observable Subspaces for 3D Human Motion Recovery

Pascal Fua, Mathieu Salzmann, Andrea Fossati

The articulated body models used to represent human motion typically have many degrees of freedom, usually expressed as joint angles that are highly correlated. The true range of motion can therefore be represented by latent variables that span a low-dimen ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2009

Motion constraint

Ronan Boulic, Daniel Raunhardt

In this paper, we propose a hybrid postural control approach taking advantage of data-driven and goal-oriented methods while overcoming their limitations. In particular, we take advantage of the latent space characterizing a given motion database. We intro ...

Springer Verlag2009

Tracking Articulated Bodies using Generalized Expectation Maximization

Pascal Fua, Andrea Fossati

A generalized expectation maximization (GEM) algorithm is used to retrieve the pose of a person from a monocular video sequence shot with a moving camera. After embedding the set of possible poses in a low dimensional space using principal component analys ...

2008

Motion Modeling: Can We Get Rid of Motion Capture?

Daniel Thalmann

For situations like crowd simulation, serious games, and VR-based training, flexible and spontaneous movements are extremely important. Motion models would be the best strategy to adopt, but unfortunately, they are very costly to develop and the results ar ...

Springer-Verlag New York, Ms Ingrid Cunningham, 175 Fifth Ave, New York, Ny 10010 Usa2008

Motion, not masking, provides the medium for feature attribution

Michael Herzog

Understanding the dynamics of how separate features combine to form holistic object representations is a central problem in visual cognition. Feature attribution (also known as feature transposition and feature inheritance) refers to the later of two stimu ...

Wiley-Blackwell2008

Low-Dimensional Motion Features for Audio-Visual Speech Recognition

Jean-Philippe Thiran, Mihai Gurban, Andrés Vallés

Audio-visual speech recognition promises to improve the performance of speech recognizers, especially when the audio is corrupted, by adding information from the visual modality, more specifically, from the video of the speaker. However, the number of visu ...

2007