TrajectoryMover: Generative Movement of Object Trajectories in Videos
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
69ccf5d93f84a0971be66d4e
Kiran Chhatre
claimed_verified
2026-04-01T12:42:21.975Z
60354a40baaf7b9681260434
https://cdn-avatars.huggingface.co/v1/production/uploads/1614105139279-noauth.jpeg
Kiran Chhatre
kiranchhatre
user
kiranchhatre
69ccf5d93f84a0971be66d4f
Hyeonho Jeong
69ccf5d93f84a0971be66d50
Yulia Gryaditskaya
69ccf5d93f84a0971be66d51
Christopher E. Peters
69ccf5d93f84a0971be66d52
Chun-Hao Paul Huang
69ccf5d93f84a0971be66d53
Paul Guerrero
TrajectoryMover: Generative Movement of Object Trajectories in Videos
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
TrajectoryAtlas enables generative video editing by generating large-scale synthetic paired video data and training a video generator to move object 3D motion trajectories while preserving plausibility and identity.
generative video editing 3D motion trajectory video generator paired video data synthetic data generation video editing trajectory movement