title: ObjectMover: Generative Object Movement with Video Prior
summary: 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.
paper_authors._id: 67d12022fa4a2571790805f9
paper_authors.name: Xin Yu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-12T15:21:02.300Z
paper_authors._id: 62d1a71ac40bde8b986c3748
paper_authors.avatarUrl: /avatars/6dbfba55563c71ed0db0d59c2629d525.svg
paper_authors.fullname: Xin Yu
paper_authors.type: user
paper_authors.user: Andyx
paper_authors._id: 67d12022fa4a2571790805fa
paper_authors.name: Tianyu Wang
paper_authors._id: 67d12022fa4a2571790805fb
paper_authors.name: Soo Ye Kim
paper_authors._id: 67d12022fa4a2571790805fc
paper_authors.name: Paul Guerrero
paper_authors._id: 67d12022fa4a2571790805fd
paper_authors.name: Xi Chen
paper_authors._id: 67d12022fa4a2571790805fe
paper_authors.name: Qing Liu
paper_authors._id: 67d12022fa4a2571790805ff
paper_authors.name: Zhe Lin
paper_authors._id: 67d12022fa4a257179080600
paper_authors.name: Xiaojuan Qi
paper_title: ObjectMover: Generative Object Movement with Video Prior
paper_summary: 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.
paper_ai_keywords: generative model sequence-to-sequence problem video generation model consistent object generation video frames real-world scenarios extreme lighting harmonization object effect movement data generation pipeline modern game engine high-quality data pairs multi-task learning strategy model generalization outstanding results