  This paper presents a novel approach to object completion, with the primary
goal of reconstructing a complete object from its partially visible components.
Our method, named MaskComp, delineates the completion process through iterative
stages of generation and segmentation. In each iteration, the object mask is
provided as an additional condition to boost image generation, and, in return,
the generated images can lead to a more accurate mask by fusing the
segmentation of images. We demonstrate that the combination of one generation
and one segmentation stage effectively functions as a mask denoiser. Through
alternation between the generation and segmentation stages, the partial object
mask is progressively refined, providing precise shape guidance and yielding
superior object completion results. Our experiments demonstrate the superiority
of MaskComp over existing approaches, e.g., ControlNet and Stable Diffusion,
establishing it as an effective solution for object completion.

Computer Vision and Pattern Recognition