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We propose a generalized approach to human gesture recognition based on multiple data modalities such as depth video, articulated pose and speech. In our system, each gesture is decomposed into large-scale body motion and local subtle movements such as hand articulation. The idea of learning at multiple scales is also applied to the temporal dimension, such that a gesture is considered as a set of characteristic motion impulses, or dynamic poses. Each modality is first processed separately indoi:10.1109/iccvw.2013.69 dblp:conf/iccvw/Neverova0PSTN13 fatcat:lerju4ym75bmjjinrwp74iaedi