ObjectMover: Generative Object Movement with Video Prior
Simple as it seems, moving an object to another location within an image is,
in fact, a challenging image-editing task that requires re-harmonizing the
lighting, adjusting the pose based on perspective, accurately filling occluded
regions, and ensuring coherent synchronization of shadows and reflections while
maintaining the object identity. In this paper, we present ObjectMover, a
generative model that can perform object movement in highly challenging scenes.
Our key insight is that we model this task as a sequence-to-sequence problem
and fine-tune a video generation model to leverage its knowledge of consistent
object generation across video frames. We show that with this approach, our
model is able to adjust to complex real-world scenarios, handling extreme
lighting harmonization and object effect movement. As large-scale data for
object movement are unavailable, we construct a data generation pipeline using
a modern game engine to synthesize high-quality data pairs. We further propose
a multi-task learning strategy that enables training on real-world video data
to improve the model generalization. Through extensive experiments, we
demonstrate that ObjectMover achieves outstanding results and adapts well to
real-world scenarios.
67d12022fa4a2571790805f9
Xin Yu
admin_assigned
2025-03-12T15:21:02.300Z
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Xin Yu
user
Andyx
67d12022fa4a2571790805fa
Tianyu Wang
67d12022fa4a2571790805fb
Soo Ye Kim
67d12022fa4a2571790805fc
Paul Guerrero
67d12022fa4a2571790805fd
Xi Chen
claimed_verified
2025-06-12T12:44:28.657Z
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xichen
user
xichenhku
67d12022fa4a2571790805fe
Qing Liu
67d12022fa4a2571790805ff
Zhe Lin
67d12022fa4a257179080600
Xiaojuan Qi
claimed_verified
2025-07-15T19:13:00.510Z
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xiaojuan qi
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xjqi
ObjectMover: Generative Object Movement with Video Prior
Simple as it seems, moving an object to another location within an image is,
in fact, a challenging image-editing task that requires re-harmonizing the
lighting, adjusting the pose based on perspective, accurately filling occluded
regions, and ensuring coherent synchronization of shadows and reflections while
maintaining the object identity. In this paper, we present ObjectMover, a
generative model that can perform object movement in highly challenging scenes.
Our key insight is that we model this task as a sequence-to-sequence problem
and fine-tune a video generation model to leverage its knowledge of consistent
object generation across video frames. We show that with this approach, our
model is able to adjust to complex real-world scenarios, handling extreme
lighting harmonization and object effect movement. As large-scale data for
object movement are unavailable, we construct a data generation pipeline using
a modern game engine to synthesize high-quality data pairs. We further propose
a multi-task learning strategy that enables training on real-world video data
to improve the model generalization. Through extensive experiments, we
demonstrate that ObjectMover achieves outstanding results and adapts well to
real-world scenarios.
ObjectMover, a sequence-to-sequence generative model fine-tuned for video, achieves excellent results in complex object movement tasks by leveraging multi-task learning and synthetic data generation.
generative model sequence-to-sequence problem fine-tune video generation model multi-task learning synthetic data generation game engine