InteractiveAvatar: Real-Time Streaming Video Generation for Consistent and Intent-Aware Avatars
This work addresses the need for visually consistent and intent-aware avatar generation in real-time interactive streaming scenarios, which is important for applications like virtual assistants and digital humans.
InteractiveAvatar introduces a real-time streaming video generation framework for avatars that maintains visual consistency over arbitrarily long durations and enables intent-aware interactions. It achieves state-of-the-art visual consistency in long-duration generation and complex real-time interaction.
Recent diffusion-based models have enabled realistic audio-driven avatar generation in real-time streaming. However, existing approaches struggle to maintain visual temporal consistency and fail to explicitly perceive user intent in complex interactive streaming scenarios. To address these challenges, we propose InteractiveAvatar, a real-time infinite-streaming video generation framework that supports visually consistent avatar video generation and intent-aware interactions. With autoregressive distillation, InteractiveAvatar achieves real-time str-eaming generation of human avatars over arbitrarily long durations. For visual consistency, we introduce a Long-Short Visual Memory (LSVM) mechanism that flexibly compresses historical visual information into compact tokens, preserving both short-range coherence and long-term consistency. To generate avatars with speeches and actions aligned with user intent, we propose a Reasoning-Reaction Module (RRM), which incorporates a State-Cycling strategy and a Cache-Switching mechanism. Extensive experimental results over diverse scenarios demonstrate that our method achieves state-of-the-art visual consistency in long-duration generation, while enabling complex user-avatar interaction in real time.