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arXiv · 2603.14331

AvatarForcing: One-Step Streaming Talking Avatars via Local-Future Sliding-Window Denoising

Liyuan Cui·Wentao Hu·Wenyuan Zhang·Zesong Yang·Fan Shi·Xiaoqiang Liu
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Abstract

Real-time talking avatar generation requires low latency and minute-level temporal stability. Autoregressive (AR) forcing enables streaming inference but suffers from exposure bias, which causes errors to accumulate and become irreversible over long rollouts. In contrast, full-sequence diffusion transformers mitigate drift but remain computationally prohibitive for real-time long-form synthesis. We present AvatarForcing, a one-step streaming diffusion framework that denoises a fixed local-future window with heterogeneous noise levels and emits one clean block per step under constant per-step cost. To stabilize unbounded streams, the method introduces dual-anchor temporal forcing: a style anchor that re-indexes RoPE to maintain a fixed relative position with respect to the active window and applies anchor-audio zero-padding, and a temporal anchor that reuses recently emitted clean blocks to ensure smooth transitions. Real-time one-step inference is enabled by two-stage streaming distillation with offline ODE backfill and distribution matching. Experiments on standard benchmarks and a new 400-video long-form benchmark show strong visual quality and lip synchronization at 34 ms/frame using a 1.3B-parameter student model for realtime streaming. Our page is available at: https://cuiliyuan121.github.io/AvatarForcing/

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Liyuan Cui, Wentao Hu, Wenyuan Zhang, Zesong Yang, Fan Shi, Xiaoqiang Liu. 2026-03-15. AvatarForcing: One-Step Streaming Talking Avatars via Local-Future Sliding-Window Denoising. https://arxiv.org/abs/2603.14331

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