The field of human-AI interaction is rapidly advancing, with a focus on developing socially intelligent AI technologies that can comprehend and generate dyadic behavioral dynamics. Researchers are working on creating models that can understand and replicate human-like interactions, including motion gestures and facial expressions, to enable more intuitive and responsive human-AI interactions. One of the key areas of research is the development of unified motion-language models that can treat human motion as a second modality, enabling effective cross-modal interaction and efficient multimodal scaling training. Another area of focus is the creation of virtual humans that can generate motion responses with both listening and speaking capabilities, making it challenging to achieve real-time and realistic interactions. Noteworthy papers include:

• Seamless Interaction, which introduces a large-scale dataset and suite of models for generating dyadic motion gestures and facial expressions.
• MotionGPT3, which proposes a bimodal motion-language model that achieves competitive performance on both motion understanding and generation tasks.
• ARIG, which presents an autoregressive framework for real-time interactive head generation.