Completing Visual Objects via Bridging Generation and Segmentation
This work addresses object completion in computer vision, offering a novel iterative approach that improves accuracy for applications requiring detailed object reconstruction.
The paper tackles the problem of reconstructing complete objects from partial visibility by introducing MaskComp, which iteratively refines object masks through alternating generation and segmentation stages, achieving superior results compared to existing methods like ControlNet and Stable Diffusion.
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.