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.

翻译：本文提出一种新颖的对象补全方法，其核心目标是从局部可见组件重构完整对象。我们提出的方法命名为MaskComp，通过生成与分割的迭代阶段来界定补全过程。在每次迭代中，对象掩码作为附加条件被输入以增强图像生成，反之，生成的图像可通过融合分割结果获得更精准的掩码。我们证明，单次生成与分割阶段的组合可有效充当掩码去噪器。通过生成与分割阶段的交替迭代，局部对象掩码逐步优化，提供精确的形状引导，从而获得更优的对象补全结果。实验表明，MaskComp优于现有方法（如ControlNet和Stable Diffusion），成为对象补全的有效解决方案。