Streaming Video Generation with Streaming Force Control

AK's Picks Embodied AI and Robotics CFCIC GenAI TDIG SCCE
2026年06月05日
我们提出了StreamForce——一种流式视频生成框架,能够通过连续的力输入实现符合物理规律的控制。与以往的视频模型不同(例如:为不同类型的力分别训练独立模型、假设力保持恒定,或依赖非因果处理方式),StreamForce是一种因果性、统一化的模型,可即时且连贯地响应局部与全局、随时间变化的力信号。为实现这一目标,我们设计了一种统一的力表征形式,将其作为控制信号,并构建了一套面向力可控视频生成的知识蒸馏流程。本模型兼顾自回归建模的高效性与对力输入的实时响应能力,在光度一致性和动力学真实性两方面均保持长期稳定。StreamForce在单块GPU上最高可实现16.6帧/秒的运行速度,在力跟踪精度与运动真实感两项指标上均达到当前最优水平。项目网站:https://neu-vi.github.io/StreamForce/
We introduce StreamForce, a streaming video generation framework that enables physically grounded control through continuous force inputs. Unlike prior video models that train separate models for different force types, assume fixed forces, or rely on non-causal processing, StreamForce is a causal and unified model that responds instantly and coherently to both local and global, time-varying forces. To achieve this, we design a unified force representation as a control signal and develop a distillation pipeline for force-controllable video generation. Our model combines autoregressive efficiency with force responsiveness, sustaining stable photometric and dynamic realism. StreamForce runs at up to 16.6 FPS on a single GPU, achieving state-of-the-art performance in both force adherence and motion realism. Project website: https://neu-vi.github.io/StreamForce/
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