Semantically Consistent Video Inpainting with Conditional Diffusion Models
arxiv(2024)
摘要
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 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.
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