GeoRemover：消除物体及其因果视觉痕迹

GeoRemover: Removing Objects and Their Causal Visual Artifacts

September 23, 2025

作者: Zixin Zhu, Haoxiang Li, Xuelu Feng, He Wu, Chunming Qiao, Junsong Yuan

cs.AI

摘要

在迈向智能图像编辑的过程中，对象移除不仅应消除目标物体，还需一并去除其引发的视觉痕迹，如阴影与反射。然而，现有的基于图像外观的方法要么严格遵循掩码对齐训练，未能移除那些未被明确掩码标记的因果效应；要么采用宽松的掩码对齐策略，缺乏可控性，可能无意中过度擦除其他对象。我们认识到，这些局限源于忽视了物体几何存在与其视觉效应之间的因果关系。为克服这一局限，我们提出了一种几何感知的两阶段框架，将对象移除分解为：(1) 几何移除与(2) 外观渲染。在第一阶段，我们通过严格掩码对齐的监督直接从几何（如深度）中移除对象，实现具有强几何约束的结构感知编辑。第二阶段，我们基于更新后的几何条件渲染出逼真的RGB图像，其中因果视觉效应作为修改后三维几何的自然结果被隐含考虑。为引导几何移除阶段的学习，我们引入了一种基于正负样本对的偏好驱动目标，鼓励模型在移除对象及其因果视觉痕迹的同时，避免引入新的结构元素。大量实验证明，我们的方法在两个流行基准测试中，在移除对象及其关联痕迹方面达到了业界领先水平。代码已发布于https://github.com/buxiangzhiren/GeoRemover。

English

Towards intelligent image editing, object removal should eliminate both the target object and its causal visual artifacts, such as shadows and reflections. However, existing image appearance-based methods either follow strictly mask-aligned training and fail to remove these causal effects which are not explicitly masked, or adopt loosely mask-aligned strategies that lack controllability and may unintentionally over-erase other objects. We identify that these limitations stem from ignoring the causal relationship between an object's geometry presence and its visual effects. To address this limitation, we propose a geometry-aware two-stage framework that decouples object removal into (1) geometry removal and (2) appearance rendering. In the first stage, we remove the object directly from the geometry (e.g., depth) using strictly mask-aligned supervision, enabling structure-aware editing with strong geometric constraints. In the second stage, we render a photorealistic RGB image conditioned on the updated geometry, where causal visual effects are considered implicitly as a result of the modified 3D geometry. To guide learning in the geometry removal stage, we introduce a preference-driven objective based on positive and negative sample pairs, encouraging the model to remove objects as well as their causal visual artifacts while avoiding new structural insertions. Extensive experiments demonstrate that our method achieves state-of-the-art performance in removing both objects and their associated artifacts on two popular benchmarks. The code is available at https://github.com/buxiangzhiren/GeoRemover.