  Human-human communication is like a delicate dance where listeners and
speakers concurrently interact to maintain conversational dynamics. Hence, an
effective model for generating listener nonverbal behaviors requires
understanding the dyadic context and interaction. In this paper, we present an
effective framework for creating 3D facial motions in dyadic interactions.
Existing work consider a listener as a reactive agent with reflexive behaviors
to the speaker's voice and facial motions. The heart of our framework is Dyadic
Interaction Modeling (DIM), a pre-training approach that jointly models
speakers' and listeners' motions through masking and contrastive learning to
learn representations that capture the dyadic context. To enable the generation
of non-deterministic behaviors, we encode both listener and speaker motions
into discrete latent representations, through VQ-VAE. The pre-trained model is
further fine-tuned for motion generation. Extensive experiments demonstrate the
superiority of our framework in generating listener motions, establishing a new
state-of-the-art according to the quantitative measures capturing the diversity
and realism of generated motions. Qualitative results demonstrate the superior
capabilities of the proposed approach in generating diverse and realistic
expressions, eye blinks and head gestures. The code is available at
https://github.com/Boese0601/Dyadic-Interaction-Modeling
