  Non verbal behaviours such as gestures, facial expressions, body posture, and
para-linguistic cues have been shown to complement or clarify verbal messages.
Hence to improve telepresence, in form of an avatar, it is important to model
these behaviours, especially in dyadic interactions. Creating such personalized
avatars not only requires to model intrapersonal dynamics between a avatar's
speech and their body pose, but it also needs to model interpersonal dynamics
with the interlocutor present in the conversation. In this paper, we introduce
a neural architecture named Dyadic Residual-Attention Model (DRAM), which
integrates intrapersonal (monadic) and interpersonal (dyadic) dynamics using
selective attention to generate sequences of body pose conditioned on audio and
body pose of the interlocutor and audio of the human operating the avatar. We
evaluate our proposed model on dyadic conversational data consisting of pose
and audio of both participants, confirming the importance of adaptive attention
between monadic and dyadic dynamics when predicting avatar pose. We also
conduct a user study to analyze judgments of human observers. Our results
confirm that the generated body pose is more natural, models intrapersonal
dynamics and interpersonal dynamics better than non-adaptive monadic/dyadic
models.
