# ObjectMover: Generative Object Movement with Video Prior

arXiv: https://arxiv.org/abs/2503.08037 · HTML: https://mlsanity.com/paper/2503.08037/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 45 of its 52 references that are in the map.
- First posted on arXiv 11 Mar 2025.

## Builds on (most cited first, 30 of 45)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://mlsanity.com/paper/2108.01073/) (2021)
- [Consistency Models](https://mlsanity.com/paper/2303.01469/) (2023)
- [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2201.09865/) (2022)
- [Palette: Image-to-Image Diffusion Models](https://mlsanity.com/paper/2111.05826/) (2021)
- [One-step Diffusion with Distribution Matching Distillation](https://mlsanity.com/paper/2311.18828/) (2023)
- [Blended Diffusion for Text-driven Editing of Natural Images](https://mlsanity.com/paper/2111.14818/) (2021)
- [WorldSimBench: Towards Video Generation Models as World Simulators](https://mlsanity.com/paper/2410.18072/) (2024)
- [Resolution-robust Large Mask Inpainting with Fourier Convolutions](https://mlsanity.com/paper/2109.07161/) (2021)
- [Improved Distribution Matching Distillation for Fast Image Synthesis](https://mlsanity.com/paper/2405.14867/) (2024)
- [Paint by Example: Exemplar-based Image Editing with Diffusion Models](https://mlsanity.com/paper/2211.13227/) (2022)
- [AnyDoor: Zero-shot Object-level Image Customization](https://mlsanity.com/paper/2307.09481/) (2023)
- [InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generation](https://mlsanity.com/paper/2309.06380/) (2023)
- [DreamSim: Learning New Dimensions of Human Visual Similarity using Synthetic Data](https://mlsanity.com/paper/2306.09344/) (2023)
- [DragDiffusion: Harnessing Diffusion Models for Interactive Point-based Image Editing](https://mlsanity.com/paper/2306.14435/) (2023)
- [MAT: Mask-Aware Transformer for Large Hole Image Inpainting](https://mlsanity.com/paper/2203.15270/) (2022)
- [Motion-I2V: Consistent and Controllable Image-to-Video Generation with Explicit Motion Modeling](https://mlsanity.com/paper/2401.15977/) (2024)
- [DragonDiffusion: Enabling Drag-style Manipulation on Diffusion Models](https://mlsanity.com/paper/2307.02421/) (2023)
- [TF-ICON: Diffusion-Based Training-Free Cross-Domain Image Composition](https://mlsanity.com/paper/2307.12493/) (2023)
- [A Task is Worth One Word: Learning with Task Prompts for High-Quality Versatile Image Inpainting](https://mlsanity.com/paper/2312.03594/) (2023)
- [DragAnything: Motion Control for Anything using Entity Representation](https://mlsanity.com/paper/2403.07420/) (2024)
- [Boximator: Generating Rich and Controllable Motions for Video Synthesis](https://mlsanity.com/paper/2402.01566/) (2024)

## Built on by (most cited first, 30 of 30)

- [PICABench: How Far Are We from Physically Realistic Image Editing?](https://mlsanity.com/paper/2510.17681/) (2025)
- [Learning an Image Editing Model without Image Editing Pairs](https://mlsanity.com/paper/2510.14978/) (2025)
- [Group Editing: Edit Multiple Images in One Go](https://mlsanity.com/paper/2603.22883/) (2026)
- [Frame In-N-Out: Unbounded Controllable Image-to-Video Generation](https://mlsanity.com/paper/2505.21491/) (2025)
- [SeqTex: Generate Mesh Textures in Video Sequence](https://mlsanity.com/paper/2507.04285/) (2025)
- [Future Optical Flow Prediction Improves Robot Control & Video Generation](https://mlsanity.com/paper/2601.10781/) (2026)
- [Dimension-Reduction Attack! Video Generative Models are Experts on Controllable Image Synthesis](https://mlsanity.com/paper/2505.23325/) (2025)
- [Points-to-3D: Structure-Aware 3D Generation with Point Cloud Priors](https://mlsanity.com/paper/2603.18782/) (2026)
- [Ctrl&Shift: High-Quality Geometry-Aware Object Manipulation in Visual Generation](https://mlsanity.com/paper/2602.11440/) (2026)
- [GMO-E2DIT: Grounded Multi-Operation Editing for E-Commerce Images](https://mlsanity.com/paper/2607.00920/) (2026)
- [Benchmarking Semantic Segmentation Models via Appearance and Geometry Attribute Editing](https://mlsanity.com/paper/2603.01535/) (2026)
- [CrimEdit: Controllable Editing for Counterfactual Object Removal, Insertion, and Movement](https://mlsanity.com/paper/2509.23708/) (2025)
- [DreamingComics: A Story Visualization Pipeline via Subject and Layout Customized Generation using Video Models](https://mlsanity.com/paper/2512.01686/) (2025)
- [EasyOmnimatte: Taming Pretrained Inpainting Diffusion Models for End-to-End Video Layered Decomposition](https://mlsanity.com/paper/2512.21865/) (2025)
- [LooseRoPE: Content-aware Attention Manipulation for Semantic Harmonization](https://mlsanity.com/paper/2601.05127/) (2026)
- [PLACID: Identity-Preserving Multi-Object Compositing via Video Diffusion with Synthetic Trajectories](https://mlsanity.com/paper/2602.00267/) (2026)
- [Geometric Image Editing via Effects-Sensitive In-Context Inpainting with Diffusion Transformers](https://mlsanity.com/paper/2602.08388/) (2026)
- [PhyEdit: Towards Real-World Object Manipulation via Physically-Grounded Image Editing](https://mlsanity.com/paper/2604.07230/) (2026)
- [GeoEdit: Geometry-Aware Object Editing via Dual-Branch Denoising](https://mlsanity.com/paper/2606.30003/) (2026)
- [Search2Motion: Training-Free Object-Level Motion Control via Attention-Consensus Search](https://mlsanity.com/paper/2603.16711/) (2026)
- [LightMover: Generative Light Movement with Color and Intensity Controls](https://mlsanity.com/paper/2603.27209/) (2026)
- [ObjectMorpher: 3D-Aware Image Editing via Deformable 3DGS Models](https://mlsanity.com/paper/2603.28152/) (2026)
- [SimInsert: Seamless Video Object Insertion via Regional Sparse Attention Fusion](https://mlsanity.com/paper/2605.23245/) (2026)
- [AlbedoEdit: Unified Instance-Level Video Editing with Albedo Guidance](https://mlsanity.com/paper/2606.01362/) (2026)
- [Bootstrap Your Generator: Unpaired Visual Editing with Flow Matching](https://mlsanity.com/paper/2606.03911/) (2026)
- [BoxCtrl: 3D-Aware Visual Prompting for Geometric Image Editing](https://mlsanity.com/paper/2606.23270/) (2026)
- [RoPEMover: Depth-Aware Object Relocation via Positional Embeddings](https://mlsanity.com/paper/2606.27332/) (2026)
- [WildShadowRemover: In-the-Wild Video Shadow Removal via Detail-Preserving Video Diffusion Models](https://mlsanity.com/paper/2607.26203/) (2026)
- [SpatialDiff: 3D-Aware Object Movement via Implicit Spatial Modeling](https://mlsanity.com/paper/2608.21776/) (2026)
- [Mover360: Controllable Object Manipulation in 360deg Panoramic Images](https://mlsanity.com/paper/2608.23238/) (2026)

## Abstract (arXiv)

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.