Flow-Guided Diffusion for Video Inpainting

• URL: http://arxiv.org/abs/2311.15368v1
• Date: Sun, 26 Nov 2023 17:48:48 GMT
• Title: Flow-Guided Diffusion for Video Inpainting
• Authors: Bohai Gu, Yongsheng Yu, Heng Fan, Libo Zhang
• Abstract summary: Video inpainting has been challenged by complex scenarios like large movements and low-light conditions. Current methods, including emerging diffusion models, face limitations in quality and efficiency. This paper introduces the Flow-Guided Diffusion model for Video Inpainting (FGDVI), a novel approach that significantly enhances temporal consistency and inpainting quality.
• Score: 15.478104117672803
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video inpainting has been challenged by complex scenarios like large movements and low-light conditions. Current methods, including emerging diffusion models, face limitations in quality and efficiency. This paper introduces the Flow-Guided Diffusion model for Video Inpainting (FGDVI), a novel approach that significantly enhances temporal consistency and inpainting quality via reusing an off-the-shelf image generation diffusion model. We employ optical flow for precise one-step latent propagation and introduces a model-agnostic flow-guided latent interpolation technique. This technique expedites denoising, seamlessly integrating with any Video Diffusion Model (VDM) without additional training. Our FGDVI demonstrates a remarkable 10% improvement in flow warping error E_warp over existing state-of-the-art methods. Our comprehensive experiments validate superior performance of FGDVI, offering a promising direction for advanced video inpainting. The code and detailed results will be publicly available in https://github.com/NevSNev/FGDVI.

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