# DragNeXt: Rethinking Drag-Based Image Editing

arXiv: https://arxiv.org/abs/2506.07611 · HTML: https://mlsanity.com/paper/2506.07611/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 39 references that are in the map.
- First posted on arXiv 9 Jun 2025.

## Builds on (most cited first, 29 of 29)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [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)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://mlsanity.com/paper/2211.09800/) (2022)
- [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022)
- [MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editing](https://mlsanity.com/paper/2304.08465/) (2023)
- [SeeSR: Towards Semantics-Aware Real-World Image Super-Resolution](https://mlsanity.com/paper/2311.16518/) (2023)
- [DragDiffusion: Harnessing Diffusion Models for Interactive Point-based Image Editing](https://mlsanity.com/paper/2306.14435/) (2023)
- [Inversion-Based Style Transfer with Diffusion Models](https://mlsanity.com/paper/2211.13203/) (2022)
- [Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold](https://mlsanity.com/paper/2305.10973/) (2023)
- [Style Injection in Diffusion: A Training-free Approach for Adapting Large-scale Diffusion Models for Style Transfer](https://mlsanity.com/paper/2312.09008/) (2023)
- [DragonDiffusion: Enabling Drag-style Manipulation on Diffusion Models](https://mlsanity.com/paper/2307.02421/) (2023)
- [DiffEditor: Boosting Accuracy and Flexibility on Diffusion-based Image Editing](https://mlsanity.com/paper/2402.02583/) (2024)
- [CoSeR: Bridging Image and Language for Cognitive Super-Resolution](https://mlsanity.com/paper/2311.16512/) (2023)
- [Drag Your Noise: Interactive Point-based Editing via Diffusion Semantic Propagation](https://mlsanity.com/paper/2404.01050/) (2024)
- [GoodDrag: Towards Good Practices for Drag Editing with Diffusion Models](https://mlsanity.com/paper/2404.07206/) (2024)
- [RegionDrag: Fast Region-Based Image Editing with Diffusion Models](https://mlsanity.com/paper/2407.18247/) (2024)
- [FreeDrag: Feature Dragging for Reliable Point-based Image Editing](https://mlsanity.com/paper/2307.04684/) (2023)
- [StableDrag: Stable Dragging for Point-based Image Editing](https://mlsanity.com/paper/2403.04437/) (2024)
- [FastDrag: Manipulate Anything in One Step](https://mlsanity.com/paper/2405.15769/) (2024)
- [ViG: Linear-complexity Visual Sequence Learning with Gated Linear Attention](https://mlsanity.com/paper/2405.18425/) (2024)
- [DragText: Rethinking Text Embedding in Point-based Image Editing](https://mlsanity.com/paper/2407.17843/) (2024)
- [CLIPDrag: Combining Text-based and Drag-based Instructions for Image Editing](https://mlsanity.com/paper/2410.03097/) (2024)
- [Advancing Incremental Few-shot Semantic Segmentation via Semantic-guided Relation Alignment and Adaptation](https://mlsanity.com/paper/2305.10868/) (2023)
- [CARE Transformer: Mobile-Friendly Linear Visual Transformer via Decoupled Dual Interaction](https://mlsanity.com/paper/2411.16170/) (2024)

## Built on by (most cited first, 6 of 6)

- [Streaming Drag-Oriented Interactive Video Manipulation: Drag Anything, Anytime!](https://mlsanity.com/paper/2510.03550/) (2025)
- [LazyDrag: Enabling Stable Drag-Based Editing on Multi-Modal Diffusion Transformers via Explicit Correspondence](https://mlsanity.com/paper/2509.12203/) (2025)
- [ContextDrag: Precise Drag-Based Image Editing via Context-Preserving Token Injection and Position-Aligned Attention](https://mlsanity.com/paper/2512.08477/) (2025)
- [RealDrag: The First Dragging Benchmark with Real Target Image](https://mlsanity.com/paper/2512.12287/) (2025)
- [In-context Region-based Drag: Drag Any Region to Any Shape](https://mlsanity.com/paper/2606.25907/) (2026)
- [ThinkDrag: Semantic Drag-Based Image Editing with Visual Reasoning](https://mlsanity.com/paper/2610.13316/) (2026)

## Abstract (arXiv)

Drag-Based Image Editing (DBIE), which allows users to manipulate images by directly dragging objects within them, has recently attracted much attention from the community. However, it faces two key challenges: (emphtextcolormagentai) point-based drag is often highly ambiguous and difficult to align with users' intentions; (emphtextcolormagentaii) current DBIE methods primarily rely on alternating between motion supervision and point tracking, which is not only cumbersome but also fails to produce high-quality results. These limitations motivate us to explore DBIE from a new perspective -- redefining it as deformation, rotation, and translation of user-specified handle regions. Thereby, by requiring users to explicitly specify both drag areas and types, we can effectively address the ambiguity issue. Furthermore, we propose a simple-yet-effective editing framework, dubbed textcolorSkyBlueDragNeXt. It unifies DBIE as a Latent Region Optimization (LRO) problem and solves it through Progressive Backward Self-Intervention (PBSI), simplifying the overall procedure of DBIE while further enhancing quality by fully leveraging region-level structure information and progressive guidance from intermediate drag states. We validate textcolorSkyBlueDragNeXt on our NextBench, and extensive experiments demonstrate that our proposed method can significantly outperform existing approaches. Code will be released on github.