InteractiveAvatar: Real-Time Streaming Video Generation for Consistent and Intent-Aware Avatars

Recent diffusion-based models have enabled realistic audio-driven avatar generation in real-time streaming. However, existing ap-proaches struggle to maintain visual temporal consistency and fail toexplicitly perceive user intent in complex interactive streaming scenar-ios. To address these challenges, we propose InteractiveAvatar, a real-time infinite-streaming video generation framework that supports vi-sually 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. Forvisual consistency, we introduce a Long-Short Visual Memory (LSVM)mechanism that flexibly compresses historical visual information intocompact tokens, preserving both short-range coherence and long-termconsistency. To generate avatars with speeches and actions aligned withuser intent, we propose a Reasoning-Reaction Module (RRM), which in-corporates a State-Cycling strategy and a Cache-Switching mechanism.Extensive experimental results over diverse scenarios demonstrate thatour method achieves state-of-the-art visual consistency in long-durationgeneration, while enabling complex user-avatar interaction in real time.