Video inpainting, which aims to fill missing regions with visually coherent content, has emerged as a crucial technique for editing and virtual tour applications. While existing approaches achieve either visual consistency or text-guided generation, they often struggle to balance between coherence and creative diversity. In this work, we introduce VideoRepainter, a two-stage framework that first allows users to inpaint a keyframe using established image-level techniques, and then propagates the corresponding change to other frames. Our approach can leverage state-of-the-art image diffusion models for keyframe manipulation, thereby easing the burden of the video-inpainting process. To this end, we integrate an image-to-video model with a symmetric condition mechanism to address ambiguity caused by direct mask downsampling. We further explore efficient strategies for mask synthesis and parameter optimization to reduce costs in data processing and model training. Evaluations demonstrate our method achieves superior results in both visual fidelity and content diversity compared to existing approaches, providing a practical solution for high-quality video editing and creation.