🤖 AI Summary
Existing object removal methods rely on manual masks or textual prompts, which often struggle to achieve precise manipulation in complex scenes, leading to incomplete removal or background distortion. This work proposes a click-based object removal approach that requires only user-provided point clicks—eliminating the need for additional training, hand-drawn masks, or textual descriptions. By leveraging a pre-trained Stable Diffusion model, the method performs target localization and background inpainting directly during the denoising process through self-attention modulation. To the best of our knowledge, this is the first technique to enable purely click-driven image editing with diffusion models, substantially lowering the usability barrier. The approach demonstrates superior performance in both quantitative evaluations and user studies, and the complete software package has been publicly released.
📝 Abstract
Existing object removal tools often rely on manual masks or text prompts, making precise removal difficult for non-expert users in complex scenes and often leading to incomplete removal or unnatural background completion. To address this issue, we present ClickRemoval, an open-source interactive object removal tool built on pretrained Stable Diffusion models and driven solely by user clicks. Without additional training, hand-drawn masks, or text descriptions, ClickRemoval localizes target objects and restores the background through self-attention modulation during denoising. Experiments show that ClickRemoval achieves competitive results across quantitative metrics and user studies. We release a complete software package at https://github.com/zld-make/ClickRemoval under the Apache-2.0 license.