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AdaptiveDrag: Semantic-Driven Dragging on Diffusion-Based Image Editing by Chen, DuoSheng; Chen, Binghui; Geng, Yifeng; Bo, Liefeng is a scholarly article available to read on EtoBox.

What is AdaptiveDrag: Semantic-Driven Dragging on Diffusion-Based Image Editing about?

Recently, several point-based image editing methods (e.g., DragDiffusion, FreeDrag, DragNoise) have emerged, yielding precise and high-quality results based on user instructions. However, these methods often make insufficient use of semantic information, leading to less desirable results. In this paper, we proposed a novel mask-free point-based image editing method, AdaptiveDrag, which provides a more flexible editing approach and generates images that better align with user intent. Specifically, we design an auto mask generation module using super-pixel division for user-friendliness. Next, we leverage a pre-trained diffusion model to optimize the latent, enabling the dragging of features from handle points to target points. To ensure a comprehensive connection between the input image and the drag process, we have developed a semantic-driven optimization. We design adaptive steps that are supervised by the positions of the points and the semantic regions derived from super-pixel segmentation. This refined optimization process also leads to more realistic and accurate drag results. Furthermore, to address the limitations in the generative consistency of the diffusion model, we intr

Author

Chen, DuoSheng; Chen, Binghui; Geng, Yifeng; Bo, Liefeng

Published

2024

Language

EN