ARIG: Autoregressive Interactive Head Generation for Real-time Conversations

Face-to-face communication, as a common human activity, motivates the research on interactive head generation. However, previous clip-wise generation paradigm or explicit listener/speaker generator-switching methods have limitations in future signal acquisition, contextual behavioral understanding, and switching smoothness, making it challenging to be real-time and realistic. In this paper, we propose an autoregressive $(A R)$ based frame-wise framework called ARIG to realize the real-time generation with better interaction realism. For real-time generation, we model motion prediction as a non-vector-quantized $A R$ process. Unlike discrete codebook-index prediction, we represent motion distribution using diffusion procedure, achieving more accurate predictions in continuous space. To improve realism, we emphasize interactive behavior understanding (IBU) and detailed conversational state understanding (CSU). In IBU, based on dual-track dual-modal signals, we summarize short-range behaviors through bidirectional-integrated learning and perform contextual understanding over long ranges. In CSU, we use voice activity signals and context features to understand the various states (interruption, feedback, pause, etc.) that exist in actual conversations. These serve as conditions for the final progressive motion prediction. Extensive experiments have verified the effectiveness of our model. The Homepage is https://jinyugy21.github.io/ARIG/.