AvatarForcing: One-Step Streaming Talking Avatars via Local-Future Sliding-Window Denoising
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 ...
| Authors | Liyuan Cui, Wentao Hu, Wenyuan Zhang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 15 March 2026 |
| Last updated at source | 18 March 2026 |
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