Inpaint-Outpaint Synergy: Mask Refinement for Trimap-Free Matting

Image matting is a fundamental task in computer vision that focuses on the precise separation of foreground objects from their backgrounds in images. This process is essential for numerous applications, such as image editing, film production, and augmented reality. Traditional methods often rely on a trimap, a predefined region that helps to distinguish the foreground from the background. However, generating an accurate trimap requires the provision of raw alpha matte, which is labor intensive and prone to drawing errors, limiting the applicability of the relevant methods in practical applications. In this paper, a novel inpaint and outpaint synergy matting approach (IOSM) is proposed to generate masks for image matting tasks without supervision by iterating through the inpaint and outpaint processes, avoiding the dependence on trimap. Specifically, the inpaint process is able to eliminate the false-positive regions present in the initial mask, while the outpaint process reduces the false-negative regions by expanding the pixels to the outer regions. The above process reduces inaccurate regions in the initial mask by means of adversarial updating, providing accurate target information for the subsequent matting stage. By iteratively combining these two processes, a more accurate mask is generated, which is then fed into the mask-to-matte (MTM) module along with the original image to obtain the final alpha matte. This approach allows for the seamless integration of the mask with the original image, improving the matting task and resulting in higher-quality matte outcomes. Experimental results demonstrate that IOSM outperforms other mainstream methods on the AIM-500, Distinctions-646 and PPM-100 datasets. Our project page is available at:https://github.com/xuecheng990531/IOSM.