ARIG: Autoregressive Interactive Head Generation for Real-time Conversations

TL;DR

ARIG introduces an autoregressive, frame-wise framework for real-time, realistic virtual agent head generation, leveraging continuous motion prediction and contextual understanding for improved interaction quality.

Contribution

It proposes a novel autoregressive, diffusion-based motion prediction framework with enhanced behavioral and conversational understanding for real-time virtual agent interactions.

Findings

Achieves more accurate, continuous motion predictions.

Enhances interaction realism through behavioral understanding.

Demonstrates effectiveness in real-time conversational scenarios.

Abstract

Face-to-face communication, as a common human activity, motivates the research on interactive head generation. A virtual agent can generate motion responses with both listening and speaking capabilities based on the audio or motion signals of the other user and itself. 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 (AR) based frame-wise framework called ARIG to realize the real-time generation with better interaction realism. To achieve real-time generation, we model motion prediction as a non-vector-quantized AR process. Unlike discrete codebook-index prediction, we represent motion distribution using diffusion procedure, achieving more accurate predictions in continuous space. To improve interaction 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 of IBU 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.