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Long-term Motion Generation for Interactive Humanoid Robots using GAN with Convolutional Network

Yusuke Nishimura, Yutaka Nakamura, Hiroshi Ishiguro

Year
2020
Citations
8

Abstract

In this report, we propose a framework for generating long-term human-like motion based on a deep generative model. Thanks to the network structure, the proposed method allows us generating seem- less long-term motions while the model is trained by 4 seconds long short motion samples. The computer graphics of generated motions seem to be reproduced scenes where a pair of persons talking to each other.

Keywords

Humanoid robotComputer scienceTerm (time)Motion (physics)Artificial intelligenceComputer visionGenerative grammarComputer graphicsGraphicsRobot

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