Semantically Consistent Video Inpainting with Conditional Diffusion Models
This work addresses video inpainting for applications needing novel content generation, representing an incremental improvement over flow- or attention-based methods.
The paper tackled the problem of video inpainting for tasks requiring novel content synthesis, which existing methods struggle with, by reframing it as a conditional generative modeling problem using diffusion models, resulting in diverse, high-quality inpaintings with spatial, temporal, and semantic consistency.
Current state-of-the-art methods for video inpainting typically rely on optical flow or attention-based approaches to inpaint masked regions by propagating visual information across frames. While such approaches have led to significant progress on standard benchmarks, they struggle with tasks that require the synthesis of novel content that is not present in other frames. In this paper, we reframe video inpainting as a conditional generative modeling problem and present a framework for solving such problems with conditional video diffusion models. We introduce inpainting-specific sampling schemes which capture crucial long-range dependencies in the context, and devise a novel method for conditioning on the known pixels in incomplete frames. We highlight the advantages of using a generative approach for this task, showing that our method is capable of generating diverse, high-quality inpaintings and synthesizing new content that is spatially, temporally, and semantically consistent with the provided context.