Motion Dreamer：基于物理一致视频生成的边界条件运动推理
Motion Dreamer: Boundary Conditional Motion Reasoning for Physically Coherent Video Generation
AI总结：
本文提出Motion Dreamer框架，通过分离运动推理与视觉合成，解决视频生成中边界条件运动推理的问题，提升运动合理性与视觉真实性。
摘要：
近年来，视频生成在自动驾驶和具身智能中的应用显示出潜力，但现实需求要求基于明确的边界条件进行物体运动推理。本文提出Motion Dreamer，一个两阶段框架，通过实例流和运动修复策略，有效整合部分用户定义运动，实现更合理的运动推理与视觉生成。实验表明，Motion Dreamer在运动合理性和视觉真实性上优于现有方法，推动了边界条件运动推理的实用化。我们的网页可访问：https://envision-research.github.io/MotionDreamer/.
英文摘要：
Recent advances in video generation have shown promise for generating future scenarios, critical for planning and control in autonomous driving and embodied intelligence. However, real-world applications demand more than visually plausible predictions; they require reasoning about object motions based on explicitly defined boundary conditions, such as initial scene image and partial object motion. We term this capability Boundary Conditional Motion Reasoning. Current approaches either neglect explicit user-defined motion constraints, producing physically inconsistent motions, or conversely demand complete motion inputs, which are rarely available in practice. Here we introduce Motion Dreamer, a two-stage framework that explicitly separates motion reasoning from visual synthesis, addressing these limitations. Our approach introduces instance flow, a sparse-to-dense motion representation enabling effective integration of partial user-defined motions, and the motion inpainting strategy to robustly enable reasoning motions of other objects. Extensive experiments demonstrate that Motion Dreamer significantly outperforms existing methods, achieving superior motion plausibility and visual realism, thus bridging the gap towards practical boundary conditional motion reasoning. Our webpage is available: https://envision-research.github.io/MotionDreamer/.
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。
