# StableDrag: Stable Dragging for Point-based Image Editing

**Authors:** Yutao Cui, Xiaotong Zhao, Guozhen Zhang, Shengming Cao, Kai Ma, Limin Wang  
**arXiv:** [2403.04437](https://arxiv.org/abs/2403.04437)  
**Date:** 2024-03-07  
**Summary page:** https://www.summarizepaper.com/en/paper/2403.04437v1/

## Key points

- Development of DragGAN sparked significant interest in point-based image editing
- DragDiffusion enhanced generative quality through adaptation of dragging techniques to diffusion models
- Challenges with dragging scheme: inaccurate point tracking and incomplete motion supervision leading to subpar outcomes
- Incorporates discriminative point tracking for improved stability in manipulation
- Implements confidence-based latent enhancement strategy for optimized quality
- Creation of two image editing models: StableDrag-GAN and StableDrag-Diff, showcasing more stable performance
- Validation of effectiveness through qualitative experiments and quantitative assessments on DragBench
- Difficulties in diffusion models include distinguishing updated points from surroundings due to noise injection and incomplete motion supervision impacting optimization of latent at certain steps
- Emphasis on implementing robust point tracking method and comprehensive motion supervision for stability and precision in future advancements

## Summary

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In the realm of point-based image editing, the development of DragGAN sparked significant interest, followed by further advancements with DragDiffusion enhancing generative quality through the adaptation of dragging techniques to diffusion models. However, despite these successes, the dragging scheme encountered two major drawbacks: inaccurate point tracking and incomplete motion supervision, leading to subpar dragging outcomes. To address these challenges, a new stable and precise drag-based editing framework called StableDrag was introduced. This framework incorporates a discriminative point tracking method to accurately locate updated handle points for improved stability in long-range manipulation. Additionally, a confidence-based latent enhancement strategy ensures optimized latent quality throughout all manipulation steps. Through unique design features, two image editing models were instantiated - StableDrag-GAN and StableDrag-Diff - showcasing more stable dragging performance. Extensive qualitative experiments and quantitative assessments conducted on DragBench validated the effectiveness of these models in achieving superior dragging outcomes. Further analysis revealed that in diffusion models, distinguishing updated points from their surroundings becomes increasingly difficult due to noise injection during the intermediate diffusion process. This can result in misleading outcomes as demonstrated by examples like the Mona Lisa portrait and vase. Additionally, incomplete motion supervision during the process may lead to inadequate optimization of latent at certain steps, impacting manipulation quality and point tracking drift. To overcome these challenges and design a more stable dragging framework, emphasis is placed on implementing a robust yet efficient point tracking method and ensuring comprehensive motion supervision throughout all manipulation steps. By adhering to these principles, future advancements in point-based image editing can strive towards achieving higher levels of stability and precision in manipulating visual content.