Crowdsourcing user studies with Mechanical Turk
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Factored conditional restricted Boltzmann Machines for modeling motion style
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Interactively optimizing information retrieval systems as a dueling bandits problem
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Seeing is believing: body motion dominates in multisensory conversations
ACM SIGGRAPH 2010 papers
A style controller for generating virtual human behaviors
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
How to train your avatar: a data driven approach to gesture generation
IVA'11 Proceedings of the 10th international conference on Intelligent virtual agents
New learning frameworks for information retrieval
New learning frameworks for information retrieval
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An effective way to build a gesture generator is to apply machine learning algorithms to derive a model. In building such a gesture generator, a common approach involves collecting a set of human conversation data and training the model to fit the data. However, after training the gesture generator, what we are looking for is whether the generated gestures are natural instead of whether the generated gestures actually fit the training data. Thus, there is a gap between the training objective and the actual goal of the gesture generator. In this work we propose an approach that use human judgment of naturalness to optimize gesture generators. We take an important step towards our goal by performing a numerical experiment to assess the optimality of the proposed framework, and the experimental results show that the framework can effectively improve the generated gestures based on the simulated naturalness criterion.