Generative video editing has enabled several intuitive editing operations for short video clips that would previously have been difficult to achieve, especially for non-expert editors. Existing methods focus on prescribing an object's 3D or 2D motion trajectory in a video, or on altering the appearance of an object or a scene, while preserving both the video's plausibility and identity. Yet a method to move an object's 3D motion trajectory in a video, i.e., moving an object while preserving its relative 3D motion, is currently still missing. The main challenge lies in obtaining paired video data for this scenario. Previous methods typically rely on clever data generation approaches to construct plausible paired data from unpaired videos, but this approach fails if one of the videos in a pair can not easily be constructed from the other. Instead, we introduce TrajectoryAtlas, a new data generation pipeline for large-scale synthetic paired video data and a video generator TrajectoryMover fine-tuned with this data. We show that this successfully enables generative movement of object trajectories. Project page: https://chhatrekiran.github.io/trajectorymover

翻译：[translated abstract in Chinese] 生成式视频编辑已经为短视频片段实现了多种直观的编辑操作，这些操作此前（尤其对于非专业编辑者而言）难以实现。现有方法主要侧重于在视频中指定物体的3D或2D运动轨迹，或改变物体或场景的外观，同时保持视频的合理性和身份特征。然而，目前仍缺乏一种能够在视频中移动物体3D运动轨迹的方法，即在保持物体相对3D运动的同时移动物体。其主要挑战在于为此场景获取成对的视频数据。以往方法通常依赖巧妙的数生成策略，从非成对视频中构建合理的成对数据，但如果一对视频中的一个无法轻易从另一个构建出来，这种方法就会失效。为此，我们引入了TrajectoryAtlas——一个用于大规模合成成对视频数据的新型数据生成流水线，以及一个基于此数据微调的视频生成器TrajectoryMover。我们证明，该方法成功实现了物体轨迹的生成式移动。项目页面：https://chhatrekiran.github.io/trajectorymover