# DragAPart: Learning a Part-Level Motion Prior for Articulated Objects

arXiv: https://arxiv.org/abs/2403.15382 · HTML: https://mlsanity.com/paper/2403.15382/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 73 of its 81 references that are in the map.
- First posted on arXiv 22 Mar 2024.

## Builds on (most cited first, 30 of 73)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022)
- [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://mlsanity.com/paper/2211.09800/) (2022)
- [Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets](https://mlsanity.com/paper/2311.15127/) (2023)
- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [Imagen Video: High Definition Video Generation with Diffusion Models](https://mlsanity.com/paper/2210.02303/) (2022)
- [Make-A-Video: Text-to-Video Generation without Text-Video Data](https://mlsanity.com/paper/2209.14792/) (2022)
- [Objaverse: A Universe of Annotated 3D Objects](https://mlsanity.com/paper/2212.08051/) (2022)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)

## Built on by (most cited first, 30 of 32)

- [Vivid-ZOO: Multi-View Video Generation with Diffusion Model](https://mlsanity.com/paper/2406.08659/) (2024)
- [InstantDrag: Improving Interactivity in Drag-based Image Editing](https://mlsanity.com/paper/2409.08857/) (2024)
- [Puppet-Master: Scaling Interactive Video Generation as a Motion Prior for Part-Level Dynamics](https://mlsanity.com/paper/2408.04631/) (2024)
- [DSO: Aligning 3D Generators with Simulation Feedback for Physical Soundness](https://mlsanity.com/paper/2503.22677/) (2025)
- [Particulate: Feed-Forward 3D Object Articulation](https://mlsanity.com/paper/2512.11798/) (2025)
- [LightningDrag: Lightning Fast and Accurate Drag-based Image Editing Emerging from Videos](https://mlsanity.com/paper/2405.13722/) (2024)
- [PartRM: Modeling Part-Level Dynamics with Large Cross-State Reconstruction Model](https://mlsanity.com/paper/2503.19913/) (2025)
- [Training-free Geometric Image Editing on Diffusion Models](https://mlsanity.com/paper/2507.23300/) (2025)
- [Motion Modes: What Could Happen Next?](https://mlsanity.com/paper/2412.00148/) (2024)
- [3D-Fixup: Advancing Photo Editing with 3D Priors](https://mlsanity.com/paper/2505.10566/) (2025)
- [DreamHOI: Subject-Driven Generation of 3D Human-Object Interactions with Diffusion Priors](https://mlsanity.com/paper/2409.08278/) (2024)
- [TokenMotion: Decoupled Motion Control via Token Disentanglement for Human-centric Video Generation](https://mlsanity.com/paper/2504.08181/) (2025)
- [C-Drag: Chain-of-Thought Driven Motion Controller for Video Generation](https://mlsanity.com/paper/2502.19868/) (2025)
- [Is Single-View Mesh Reconstruction Ready for Robotics?](https://mlsanity.com/paper/2505.17966/) (2025)
- [GPS as a Control Signal for Image Generation](https://mlsanity.com/paper/2501.12390/) (2025)
- [Advances in 4D Representation: Geometry, Motion, and Interaction](https://mlsanity.com/paper/2510.19255/) (2025)
- [Seed3D 2.0: Advancing High-Fidelity Simulation-Ready 3D Content Generation](https://mlsanity.com/paper/2605.13862/) (2026)
- [Instruct-Particulate: Scaling Feed-Forward 3D Object Articulation with Kinematic Control](https://mlsanity.com/paper/2606.14699/) (2026)
- [DragMesh: Interactive 3D Generation Made Easy](https://mlsanity.com/paper/2512.06424/) (2025)
- [DYNAMO: Dependency-Aware Deep Learning Framework for Articulated Assembly Motion Prediction](https://mlsanity.com/paper/2509.12430/) (2025)
- [What If : Understanding Motion Through Sparse Interactions](https://mlsanity.com/paper/2510.12777/) (2025)
- [LooseControlVideo: Directorial Video Control using Spatial Blocking](https://mlsanity.com/paper/2606.19495/) (2026)
- [RealDrag: The First Dragging Benchmark with Real Target Image](https://mlsanity.com/paper/2512.12287/) (2025)
- [Sketch2Arti: Sketch-based Articulation Modeling of CAD Objects](https://mlsanity.com/paper/2604.25781/) (2026)
- [Articulated Object Reconstruction from Rest-State Observation](https://mlsanity.com/paper/2607.27749/) (2026)
- [GIFT: Generated Indoor video frames for Texture-less point tracking](https://mlsanity.com/paper/2503.12944/) (2025)
- [MotionDiff: Training-free Zero-shot Interactive Motion Editing via Flow-assisted Multi-view Diffusion](https://mlsanity.com/paper/2503.17695/) (2025)
- [Motion Marionette: Rethinking Rigid Motion Transfer via Prior Guidance](https://mlsanity.com/paper/2511.19909/) (2025)
- [DynaDrag: Dynamic Drag-Style Image Editing by Motion Prediction](https://mlsanity.com/paper/2601.00542/) (2026)
- [DailyArt: Discovering Articulation from Single Static Images via Latent Dynamics](https://mlsanity.com/paper/2604.07758/) (2026)

## Abstract (arXiv)

We introduce DragAPart, a method that, given an image and a set of drags as input, generates a new image of the same object that responds to the action of the drags. Differently from prior works that focused on repositioning objects, DragAPart predicts part-level interactions, such as opening and closing a drawer. We study this problem as a proxy for learning a generalist motion model, not restricted to a specific kinematic structure or object category. We start from a pre-trained image generator and fine-tune it on a new synthetic dataset, Drag-a-Move, which we introduce. Combined with a new encoding for the drags and dataset randomization, the model generalizes well to real images and different categories. Compared to prior motion-controlled generators, we demonstrate much better part-level motion understanding.