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This paper investigates using a Transformer-based generative model to learn emotional body motions from motion-capture data of Japanese actors, generating motions conditioned on discrete emotion labels. Evaluations show the generated motions improve emotion recognition when used for data augmentation and enable smooth transitions between emotion intensities.