结绳强制：驯服自回归视频扩散模型以实现实时无限交互肖像动画
Knot Forcing: Taming Autoregressive Video Diffusion Models for Real-time Infinite Interactive Portrait Animation
AI总结：
Knot Forcing通过分块生成、时间结模块和提前运行机制，解决实时肖像动画中的时间一致性、误差累积和长期一致性问题，实现高保真、低延迟的无限交互动画。
摘要：
实时肖像动画对于虚拟助手和实时化身等交互应用至关重要，要求具有高视觉保真度、时间一致性、超低延迟以及对动态输入（如参考图像和驱动信号）的响应控制。虽然扩散模型能够实现高质量的输出，但其非因果性质阻碍了流式部署。因果自回归视频生成方法虽然能够实现逐帧高效生成，但存在误差累积、块边界处运动不连续以及长期一致性下降的问题。在本文中，我们提出了一种名为Knot Forcing的新流式框架，用于实时肖像动画，通过三个关键设计解决了这些问题：（1）一种分块生成策略，通过缓存参考图像的KV状态实现全局身份保持，并利用滑动窗口注意力进行局部时间建模；（2）一个时间结模块，通过图像到视频的条件化重叠相邻块并传播时空提示以平滑块间运动过渡；（3）一个"提前运行"机制，该机制在推理过程中动态更新参考帧的时间坐标，使其语义上下文始终保持在当前运行帧之前，以支持长期一致性。Knot Forcing能够在无限序列上实现高保真、时间一致且交互性的肖像动画，使用消费级GPU实现了实时性能，具有强大的视觉稳定性。
英文摘要：
Real-time portrait animation is essential for interactive applications such as virtual assistants and live avatars, requiring high visual fidelity, temporal coherence, ultra-low latency, and responsive control from dynamic inputs like reference images and driving signals. While diffusion-based models achieve strong quality, their non-causal nature hinders streaming deployment. Causal autoregressive video generation approaches enable efficient frame-by-frame generation but suffer from error accumulation, motion discontinuities at chunk boundaries, and degraded long-term consistency. In this work, we present a novel streaming framework named Knot Forcing for real-time portrait animation that addresses these challenges through three key designs: (1) a chunk-wise generation strategy with global identity preservation via cached KV states of the reference image and local temporal modeling using sliding window attention; (2) a temporal knot module that overlaps adjacent chunks and propagates spatio-temporal cues via image-to-video conditioning to smooth inter-chunk motion transitions; and (3) A "running ahead" mechanism that dynamically updates the reference frame's temporal coordinate during inference, keeping its semantic context ahead of the current rollout frame to support long-term coherence. Knot Forcing enables high-fidelity, temporally consistent, and interactive portrait animation over infinite sequences, achieving real-time performance with strong visual stability on consumer-grade GPUs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
