FacePlex: Toward Natural Full-Duplex Conversational Avatars

Abstract

Natural human conversation is inherently a real-time interaction in which speech and facial behavior continuously evolve. Enabling such interaction requires a conversational avatar to jointly generate speech and facial motion in real time, prepare facial motion for upcoming speech before the corresponding audio is emitted, and produce non-verbal reactions that reflect the ongoing dialogue. However, existing conversational avatar systems cannot address such requirements: audio-driven methods rely on pre-given speech, while joint streaming generation alone does not ensure anticipatory articulation or semantically appropriate reactions. We propose $\textbf{FacePlex}$, a unified framework for full-duplex speech-facial motion generation and real-time avatar rendering. FacePlex jointly coordinates speech, facial motion, and Gaussian splatting rendering on a shared streaming timeline. To prepare facial motion for upcoming speech, FacePlex predicts a short speech continuation and uses its future hidden states through asymmetric conditioning and denoising, while continuously updating them as new user audio arrives without observing future user input. For dialogue-grounded non-verbal behavior, we construct $\textit{SemReact}$, a dataset aligning dialogue context, reaction semantics, and facial motion, and introduce a semantic behavior router that guides continuous motion during both speaking and listening. Extensive experiments show improved audio-visual synchronization and facial articulation, natural dialogue-grounded non-verbal responses, and low-latency of 122 ms end-to-end avatar interaction.