CoIn: Comprehensive 2D-3D Inpainting with Gaussian Splatting Guidance

3D scene inpainting is essential for reconstructing areas cor-rupted by occlusions or limited viewpoints. While recent methods lever-age Gaussian Splatting (GS) for efficient 3D editing, they often de-pend on precise multi-view segmentation masks and are inherently con-strained to object removal tasks. We propose CoIn, a novel frameworkthat bridges 2D inpainting models and 3DGS through a multi-stage con-sistency pipeline. Our approach first generates initial inpainted imagesusing a diffusion model, enabling the use of arbitrary-shaped masks anddiverse tasks like object insertion. We then introduce Reference Adap-tive GS with Feature Attention to reconstruct a coarse 3D scene byadaptively weighing towards a reference view (2D → 3D). This 3D rep-resentation provides geometric guidance to the diffusion process via GS-based Reference Feature Warping, ensuring multi-view consistency (3D→ 2D). Finally, a Texture-Enhancing Discriminator refines the 3D sceneto achieve high photometric realism (2D → 3D). Experiments show thatCoIn, effectively leveraging bidirectional information flow, achieves state-of-the-art performance and effectively handles both object removal andobject insertion with flexible mask input.