We propose a pipeline that leverages Stable Diffusion to improve inpainting results in the context of defurnishing -- the removal of furniture items from indoor panorama images. Specifically, we illustrate how increased context, domain-specific model fine-tuning, and improved image blending can produce high-fidelity inpaints that are geometrically plausible without needing to rely on room layout estimation. We demonstrate qualitative and quantitative improvements over other furniture removal techniques.

翻译：我们提出一种利用Stable Diffusion改进室内全景图像中家具移除（即清空家具）任务修复效果的流程。具体而言，我们展示了通过增加上下文信息、领域特定模型微调以及改进图像融合技术，如何无需依赖房间布局估计即可生成几何特征合理且高保真的修复结果。我们通过定性和定量实验证明了该方法相对于其他家具移除技术的优势。