TrajectoryMover: 视频中物体轨迹的生成性运动
TrajectoryMover: Generative Movement of Object Trajectories in Videos
AI总结：
本文提出TrajectoryMover，一种生成视频中物体轨迹运动的方法，通过生成大规模合成配对视频数据和细调的视频生成器，实现了物体轨迹的生成性移动。
摘要：
生成性视频编辑已经使一些直观的编辑操作成为可能，这些操作以前在短视频片段中难以实现，特别是对于非专业编辑者而言。现有方法专注于在视频中为对象的3D或2D运动轨迹指定路径，或改变对象或场景的外观，同时保持视频的合理性和身份。然而，目前仍缺少一种方法，可以在视频中移动对象的3D运动轨迹，即在保持其相对3D运动的情况下移动对象。主要挑战在于获取这种场景下的配对视频数据。先前的方法通常依赖于巧妙的数据生成方法，从不成对的视频中构造出合理的配对数据，但这种方法在无法从另一视频轻易构造出配对视频时会失效。相反，我们引入了TrajectoryAtlas，一种新的大规模合成配对视频数据生成管道，以及一个通过此数据细调的视频生成器TrajectoryMover。我们证明这种方法成功实现了物体轨迹的生成性移动。项目页面：https://chhatrekiran.github.io/trajectorymover
英文摘要：
Generative video editing has enabled creative control over an object's position in a video by prescribing an object's 3D or 2D motion trajectory, while preserving both video plausibility and identity. However, manually specifying a plausible full motion trajectory, like the arcs of a bouncing ball, requires time and expertise, and may therefore not be a suitable editing task for non-experts or quick edits. In contrast, in the image domain, generative object translation has been established as a simple editing task that requires only a single drag to move an object to a new location; yet an equivalent method is still missing for videos. We propose an analogous new editing task for videos that translates an object's 3D motion trajectory in a video while preserving identity and plausibility of appearance and motion, for example, translating the trajectory of a bouncing ball while preserving its relative motion. The main challenge in training this task lies in obtaining paired video data for this scenario. We introduce TrajectorySynth, a new data generation strategy for large-scale synthetic paired video data and a video generator TrajectoryMover fine-tuned with this data. We show that this enables generative movement of object trajectories. Project Page: https://chhatrekiran.github.io/trajectorymover
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。
