Generating expressive body movements of a pianist for a given symbolic sequence of key depressions is important for music interaction, but most existing methods cannot incorporate musical context information and generate movements of body joints that are further away from the fingers such as head and shoulders. This paper addresses such limitations by directly training a deep neural network system to map a MIDI note stream and additional metric structures to a skeleton sequence of a pianist playing a keyboard instrument in an online fashion. Experiments show that (a) incorporation of metric information yields in 4% smaller error, (b) the model is capable of learning the motion behavior of a specific player, and (c) no significant difference between the generated and real human movements is observed by human subjects in 75% of the pieces.