# LeviTor: 3D Trajectory Oriented Image-to-Video Synthesis

arXiv: https://arxiv.org/abs/2412.15214 · HTML: https://mlsanity.com/paper/2412.15214/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- Builds on 62 of its 67 references that are in the map.
- First posted on arXiv 19 Dec 2024.

## Builds on (most cited first, 30 of 62)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets](https://mlsanity.com/paper/2311.15127/) (2023)
- [Video Diffusion Models](https://mlsanity.com/paper/2204.03458/) (2022)
- [CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer](https://mlsanity.com/paper/2408.06072/) (2024)
- [Make-A-Video: Text-to-Video Generation without Text-Video Data](https://mlsanity.com/paper/2209.14792/) (2022)
- [Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2304.08818/) (2023)
- [AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning](https://mlsanity.com/paper/2307.04725/) (2023)
- [Depth Anything V2](https://mlsanity.com/paper/2406.09414/) (2024)
- [Towards Accurate Generative Models of Video: A New Metric & Challenges](https://mlsanity.com/paper/1812.01717/) (2018)
- [CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers](https://mlsanity.com/paper/2205.15868/) (2022)
- [Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation](https://mlsanity.com/paper/2212.11565/) (2022)
- [Accelerating 3D Deep Learning with PyTorch3D](https://mlsanity.com/paper/2007.08501/) (2020)
- [Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation](https://mlsanity.com/paper/2311.17117/) (2023)
- [Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators](https://mlsanity.com/paper/2303.13439/) (2023)
- [ModelScope Text-to-Video Technical Report](https://mlsanity.com/paper/2308.06571/) (2023)
- [MotionCtrl: A Unified and Flexible Motion Controller for Video Generation](https://mlsanity.com/paper/2312.03641/) (2023)
- [VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models](https://mlsanity.com/paper/2401.09047/) (2024)
- [VideoCrafter1: Open Diffusion Models for High-Quality Video Generation](https://mlsanity.com/paper/2310.19512/) (2023)
- [CoTracker: It is Better to Track Together](https://mlsanity.com/paper/2307.07635/) (2023)
- [Phenaki: Variable Length Video Generation From Open Domain Textual Description](https://mlsanity.com/paper/2210.02399/) (2022)
- [DynamiCrafter: Animating Open-domain Images with Video Diffusion Priors](https://mlsanity.com/paper/2310.12190/) (2023)
- [MagicVideo: Efficient Video Generation With Latent Diffusion Models](https://mlsanity.com/paper/2211.11018/) (2022)
- [I2VGen-XL: High-Quality Image-to-Video Synthesis via Cascaded Diffusion Models](https://mlsanity.com/paper/2311.04145/) (2023)
- [LAVIE: High-Quality Video Generation with Cascaded Latent Diffusion Models](https://mlsanity.com/paper/2309.15103/) (2023)

## Built on by (most cited first, 30 of 39)

- [Controllable Video Generation: A Survey](https://mlsanity.com/paper/2507.16869/) (2025)
- [MagicMotion: Controllable Video Generation with Dense-to-Sparse Trajectory Guidance](https://mlsanity.com/paper/2503.16421/) (2025)
- [Wan-Move: Motion-controllable Video Generation via Latent Trajectory Guidance](https://mlsanity.com/paper/2512.08765/) (2025)
- [ATI: Any Trajectory Instruction for Controllable Video Generation](https://mlsanity.com/paper/2505.22944/) (2025)
- [Learning Video Generation for Robotic Manipulation with Collaborative Trajectory Control](https://mlsanity.com/paper/2506.01943/) (2025)
- [Exploring the Evolution of Physics Cognition in Video Generation: A Survey](https://mlsanity.com/paper/2503.21765/) (2025)
- [VerseCrafter: Dynamic Realistic Video World Model with 4D Geometric Control](https://mlsanity.com/paper/2601.05138/) (2026)
- [VidCRAFT3: Camera, Object, and Lighting Control for Image-to-Video Generation](https://mlsanity.com/paper/2502.07531/) (2025)
- [Perception-as-Control: Fine-grained Controllable Image Animation with 3D-aware Motion Representation](https://mlsanity.com/paper/2501.05020/) (2025)
- [Generative Video Motion Editing with 3D Point Tracks](https://mlsanity.com/paper/2512.02015/) (2025)
- [BridgeV2W: Bridging Video Generation Models to Embodied World Models via Embodiment Masks](https://mlsanity.com/paper/2602.03793/) (2026)
- [Motion-Aware Generative Frame Interpolation](https://mlsanity.com/paper/2501.03699/) (2025)
- [Tora2: Motion and Appearance Customized Diffusion Transformer for Multi-Entity Video Generation](https://mlsanity.com/paper/2507.05963/) (2025)
- [Frame In-N-Out: Unbounded Controllable Image-to-Video Generation](https://mlsanity.com/paper/2505.21491/) (2025)
- [O-DisCo-Edit: Object Distortion Control for Unified Realistic Video Editing](https://mlsanity.com/paper/2509.01596/) (2025)
- [SymphoMotion: Joint Control of Camera Motion and Object Dynamics for Coherent Video Generation](https://mlsanity.com/paper/2604.03723/) (2026)
- [Zo3T: Zero-Shot 3D-Aware Trajectory-Guided Image-to-Video Generation via Test-Time Training](https://mlsanity.com/paper/2509.06723/) (2025)
- [Real-Time Motion-Controllable Autoregressive Video Diffusion](https://mlsanity.com/paper/2510.08131/) (2025)
- [The World is Your Canvas: Painting Promptable Events with Reference Images, Trajectories, and Text](https://mlsanity.com/paper/2512.16924/) (2025)
- [FlexTraj: Image-to-Video Generation with Flexible Point Trajectory Control](https://mlsanity.com/paper/2510.08527/) (2025)
- [Planning with Sketch-Guided Verification for Physics-Aware Video Generation](https://mlsanity.com/paper/2511.17450/) (2025)
- [DiTraj: training-free trajectory control for video diffusion transformer](https://mlsanity.com/paper/2509.21839/) (2025)
- [PoseAnything: Universal Pose-guided Video Generation with Part-aware Temporal Coherence](https://mlsanity.com/paper/2512.13465/) (2025)
- [FlashMotion: Few-Step Controllable Video Generation with Trajectory Guidance](https://mlsanity.com/paper/2603.12146/) (2026)
- [WorldDirector: Building Controllable World Simulators with Persistent Dynamic Memory](https://mlsanity.com/paper/2607.02517/) (2026)
- [Consistent and Controllable Image Animation with Motion Linear Diffusion Transformers](https://mlsanity.com/paper/2508.07246/) (2025)
- [TRACE: Object Motion Editing in Videos with First-Frame Trajectory Guidance](https://mlsanity.com/paper/2603.25707/) (2026)
- [Image-to-Video Diffusion: From Foundations to Open Frontiers](https://mlsanity.com/paper/2605.17248/) (2026)
- [DRAW2ACT: Turning Depth-Encoded Trajectories into Robotic Demonstration Videos](https://mlsanity.com/paper/2512.14217/) (2025)
- [Go-with-the-Track: Video Compositing and Motion Control with Point Tracking](https://mlsanity.com/paper/2606.20891/) (2026)

## Abstract (arXiv)

The intuitive nature of drag-based interaction has led to its growing adoption for controlling object trajectories in image-to-video synthesis. Still, existing methods that perform dragging in the 2D space usually face ambiguity when handling out-of-plane movements. In this work, we augment the interaction with a new dimension, i.e., the depth dimension, such that users are allowed to assign a relative depth for each point on the trajectory. That way, our new interaction paradigm not only inherits the convenience from 2D dragging, but facilitates trajectory control in the 3D space, broadening the scope of creativity. We propose a pioneering method for 3D trajectory control in image-to-video synthesis by abstracting object masks into a few cluster points. These points, accompanied by the depth information and the instance information, are finally fed into a video diffusion model as the control signal. Extensive experiments validate the effectiveness of our approach, dubbed LeviTor, in precisely manipulating the object movements when producing photo-realistic videos from static images. Our code is available at: https://github.com/ant-research/LeviTor.