# TrajectoryMover: Generative Movement of Object Trajectories in Videos

arXiv: https://arxiv.org/abs/2603.29092 · HTML: https://mlsanity.com/paper/2603.29092/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 59 references that are in the map.
- First posted on arXiv 31 Mar 2026.

## Builds on (most cited first, 30 of 35)

- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021)
- [Wan: Open and Advanced Large-Scale Video Generative Models](https://mlsanity.com/paper/2503.20314/) (2025)
- [InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks](https://mlsanity.com/paper/2312.14238/) (2023)
- [Objaverse: A Universe of Annotated 3D Objects](https://mlsanity.com/paper/2212.08051/) (2022)
- [Resolution-robust Large Mask Inpainting with Fourier Convolutions](https://mlsanity.com/paper/2109.07161/) (2021)
- [LTX-Video: Realtime Video Latent Diffusion](https://mlsanity.com/paper/2501.00103/) (2024)
- [VACE: All-in-One Video Creation and Editing](https://mlsanity.com/paper/2503.07598/) (2025)
- [ReCamMaster: Camera-Controlled Generative Rendering from A Single Video](https://mlsanity.com/paper/2503.11647/) (2025)
- [Motion-I2V: Consistent and Controllable Image-to-Video Generation with Explicit Motion Modeling](https://mlsanity.com/paper/2401.15977/) (2024)
- [Video Depth Anything: Consistent Depth Estimation for Super-Long Videos](https://mlsanity.com/paper/2501.12375/) (2025)
- [Diffusion as Shader: 3D-aware Video Diffusion for Versatile Video Generation Control](https://mlsanity.com/paper/2501.03847/) (2025)
- [Motion Prompting: Controlling Video Generation with Motion Trajectories](https://mlsanity.com/paper/2412.02700/) (2024)
- [FLATTEN: optical FLow-guided ATTENtion for consistent text-to-video editing](https://mlsanity.com/paper/2310.05922/) (2023)
- [Go-with-the-Flow: Motion-Controllable Video Diffusion Models Using Real-Time Warped Noise](https://mlsanity.com/paper/2501.08331/) (2025)
- [MotionStream: Real-Time Video Generation with Interactive Motion Controls](https://mlsanity.com/paper/2511.01266/) (2025)
- [Ground-A-Video: Zero-shot Grounded Video Editing using Text-to-image Diffusion Models](https://mlsanity.com/paper/2310.01107/) (2023)
- [I2VEdit: First-Frame-Guided Video Editing via Image-to-Video Diffusion Models](https://mlsanity.com/paper/2405.16537/) (2024)
- [EditVerse: Unifying Image and Video Editing and Generation with In-Context Learning](https://mlsanity.com/paper/2509.20360/) (2025)
- [Control-A-Video: Controllable Text-to-Video Diffusion Models with Motion Prior and Reward Feedback Learning](https://mlsanity.com/paper/2305.13840/) (2023)
- [StyleMaster: Stylize Your Video with Artistic Generation and Translation](https://mlsanity.com/paper/2412.07744/) (2024)
- [ATI: Any Trajectory Instruction for Controllable Video Generation](https://mlsanity.com/paper/2505.22944/) (2025)
- [DragVideo: Interactive Drag-style Video Editing](https://mlsanity.com/paper/2312.02216/) (2023)
- [Generative Video Propagation](https://mlsanity.com/paper/2412.19761/) (2024)
- [Reangle-A-Video: 4D Video Generation as Video-to-Video Translation](https://mlsanity.com/paper/2503.09151/) (2025)
- [Generative Video Motion Editing with 3D Point Tracks](https://mlsanity.com/paper/2512.02015/) (2025)
- [Drag-A-Video: Non-rigid Video Editing with Point-based Interaction](https://mlsanity.com/paper/2312.02936/) (2023)
- [OnlyFlow: Optical Flow based Motion Conditioning for Video Diffusion Models](https://mlsanity.com/paper/2411.10501/) (2024)
- [VideoHandles: Editing 3D Object Compositions in Videos Using Video Generative Priors](https://mlsanity.com/paper/2503.01107/) (2025)
- [MotionV2V: Editing Motion in a Video](https://mlsanity.com/paper/2511.20640/) (2025)

## Built on by (most cited first, 1 of 1)

- [Joint and Cross-Modal Video-Audio Generation and Editing: A Unified Formulation and Design Taxonomy](https://mlsanity.com/paper/2609.34381/) (2026)

## Abstract (arXiv)

Generative video editing has enabled creative control over an object's position in a video by prescribing an object's 3D or 2D motion trajectory, while preserving both video plausibility and identity. However, manually specifying a plausible full motion trajectory, like the arcs of a bouncing ball, requires time and expertise, and may therefore not be a suitable editing task for non-experts or quick edits. In contrast, in the image domain, generative object translation has been established as a simple editing task that requires only a single drag to move an object to a new location; yet an equivalent method is still missing for videos. We propose an analogous new editing task for videos that translates an object's 3D motion trajectory in a video while preserving identity and plausibility of appearance and motion, for example, translating the trajectory of a bouncing ball while preserving its relative motion. The main challenge in training this task lies in obtaining paired video data for this scenario. We introduce TrajectorySynth, a new data generation strategy for large-scale synthetic paired video data and a video generator TrajectoryMover fine-tuned with this data. We show that this enables generative movement of object trajectories. Project Page: https://chhatrekiran.github.io/trajectorymover