Avatar Forcing: Real-Time Interactive Head Avatar Generation for Natural Conversation

TL;DR

This paper introduces Avatar Forcing, a real-time interactive head avatar generation framework that enables lifelike, expressive reactions in virtual communication by processing multimodal inputs with low latency and learning without labeled data.

Contribution

It presents a novel diffusion forcing approach for real-time avatar interaction and a label-free learning method for expressive reactions, advancing interactive virtual avatar technology.

Findings

Achieves approximately 500ms latency in avatar reactions.

Provides 6.8X speedup over baseline methods.

Over 80% user preference for reactive and expressive avatars.

Abstract

Talking head generation creates lifelike avatars from static portraits for virtual communication and content creation. However, current models do not yet convey the feeling of truly interactive communication, often generating one-way responses that lack emotional engagement. We identify two key challenges toward truly interactive avatars: generating motion in real-time under causal constraints and learning expressive, vibrant reactions without additional labeled data. To address these challenges, we propose Avatar Forcing, a new framework for interactive head avatar generation that models real-time user-avatar interactions through diffusion forcing. This design allows the avatar to process real-time multimodal inputs, including the user's audio and motion, with low latency for instant reactions to both verbal and non-verbal cues such as speech, nods, and laughter. Furthermore, we introduce a direct preference optimization method that leverages synthetic losing samples constructed by dropping user conditions, enabling label-free learning of expressive interaction. Experimental results demonstrate that our framework enables real-time interaction with low latency (approximately 500ms), achieving 6.8X speedup compared to the baseline, and produces reactive and expressive avatar motion, which is preferred over 80% against the baseline.