EntityErasure: Erasing Entity Cleanly via Amodal Entity Segmentation and Completion

Yixing Zhu, Qing Zhang, Yitong Wang, Yongwei Nie, Wei-Shi Zheng; Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR), 2025, pp. 28274-28283

This paper presents EntityErasure, a novel diffusion-based inpainting method that can effectively erase entities without inducing unwanted sundries. To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as reference, avoiding the possibility to generate unpredictable sundries. Moreover, we develop two entity segmentation based metrics for quantitatively assessing the performance of object erasure, which are shown be more effective than existing metrics. Experimental results demonstrate that our approach outperforms other state-of-the-art object erasure methods. Our code and data are available at https://zyxunh.github.io/EntityErasure-ProjectPage/.