Current motion-controlled image-to-video generation models rigidly follow user-provided trajectories that are often sparse, imprecise, and causally incomplete. Such reliance often yields unnatural or implausible outcomes, especially by missing secondary causal consequences. To address this, we introduce MotiMotion, a novel framework that reformulates motion control as a reasoning-then-generation problem. To encourage causally grounded and commonsense-consistent interactions, we leverage a training-free vision-language reasoner to refine image-space coordinates of primary trajectories and to hallucinate plausible secondary motions. To further improve motion naturalness, we propose a confidence-aware control scheme that modulates guidance strength, enabling the model to closely follow high-confidence plans while correcting artifacts under low-confidence inputs with its internal generative priors. To support systematic evaluation, we curate a new image-to-video benchmark, MotiBench, consisting of interaction-centric scenes where new events are triggered by motion. Both VLM-based evaluation and a human study on MotiBench demonstrate that MotiMotion produces videos with more plausible object behaviors and interaction, and is preferred over existing approaches.

翻译：当前的运动控制图像到视频生成模型严格遵循用户提供的轨迹，这些轨迹往往稀疏、不精确且因果不完整。这种依赖常导致生成结果不自然或不可信，尤其会遗漏次要因果后果。为解决这一问题，我们提出MotiMotion，一种将运动控制重构为"推理-生成"问题的新型框架。为鼓励因果合理且符合常识的交互，我们利用无需训练的视觉语言推理器，优化主轨迹的图像空间坐标，并合理推测次要运动。为进一步提升运动自然性，我们提出置信度感知的控制方案，通过调节引导强度，使模型在高置信度规划下紧密遵循轨迹，而在低置信度输入下利用内部生成先验修正伪影。为支持系统评估，我们构建了新的图像到视频基准MotiBench，包含以交互为中心的场景，其中新事件由运动触发。在MotiBench上的VLM评估与人类研究均表明，MotiMotion生成的视频具有更合理的物体行为与交互表现，并获得优于现有方法的偏好选择。