Recent advances in video generation have led to remarkable improvements in visual quality and temporal coherence. Upon this, trajectory-controllable video generation has emerged to enable precise object motion control through explicitly defined spatial paths. However, existing methods struggle with complex object movements and multi-object motion control, resulting in imprecise trajectory adherence, poor object consistency, and compromised visual quality. Furthermore, these methods only support trajectory control in a single format, limiting their applicability in diverse scenarios. Additionally, there is no publicly available dataset or benchmark specifically tailored for trajectory-controllable video generation, hindering robust training and systematic evaluation. To address these challenges, we introduce MagicMotion, a novel image-to-video generation framework that enables trajectory control through three levels of conditions from dense to sparse: masks, bounding boxes, and sparse boxes. Given an input image and trajectories, MagicMotion seamlessly animates objects along defined trajectories while maintaining object consistency and visual quality. Furthermore, we present MagicData, a large-scale trajectory-controlled video dataset, along with an automated pipeline for annotation and filtering. We also introduce MagicBench, a comprehensive benchmark that assesses both video quality and trajectory control accuracy across different numbers of objects. Extensive experiments demonstrate that MagicMotion outperforms previous methods across various metrics. Our project page are publicly available at https://quanhaol.github.io/magicmotion-site.

翻译：近期视频生成技术的进展在视觉质量和时序连贯性方面取得了显著提升。在此基础上，轨迹可控视频生成方法应运而生，通过明确定义的空间路径实现对物体运动的精确控制。然而，现有方法在处理复杂物体运动和多物体运动控制时存在局限，导致轨迹跟随不精确、物体一致性差以及视觉质量受损。此外，这些方法仅支持单一格式的轨迹控制，限制了其在多样化场景中的应用。同时，目前缺乏专门针对轨迹可控视频生成任务公开的数据集或基准测试，阻碍了鲁棒训练和系统化评估。为应对这些挑战，我们提出MagicMotion——一种新颖的图像到视频生成框架，通过从稠密到稀疏的三个层次条件（掩码、边界框和稀疏框）实现轨迹控制。给定输入图像和轨迹，MagicMotion能够沿定义轨迹流畅地动画化物体，同时保持物体一致性和视觉质量。此外，我们构建了MagicData——一个大规模轨迹控制视频数据集，并提供了自动化标注与筛选流程。我们还提出了MagicBench——一个综合评估视频质量和不同物体数量下轨迹控制精度的基准测试。大量实验表明，MagicMotion在多项指标上均优于现有方法。项目页面已公开于https://quanhaol.github.io/magicmotion-site。