TrajectoryMover: Generative Movement of Object Trajectories in Videos

2026-03-30

AI Summary

Existing video editing methods struggle to globally translate the 3D trajectories of objects while preserving their relative motion structure and scene plausibility. To address this challenge, this work proposes TrajectoryMover, a generative model–based approach for controllable video editing. By constructing TrajectoryAtlas—a large-scale synthetic paired dataset—and incorporating 3D motion consistency constraints during fine-tuning, TrajectoryMover achieves, for the first time, holistic 3D trajectory transfer of objects in videos. The method significantly outperforms existing techniques while maintaining object identity, motion semantics, and visual realism, thereby establishing a new paradigm for high-fidelity, controllable video editing.

Abstract

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

Problem

• object trajectory
• 3D motion
• video editing
• generative movement
• paired video data

Innovation

• generative video editing
• object trajectory manipulation
• 3D motion preservation
• synthetic paired data
• TrajectoryMover