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arXiv · 2411.18745

DiffMVR: Diffusion-based Automated Multi-Guidance Video Restoration

Zheyan Zhang·Diego Klabjan·Renee CB Manworren
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Abstract

In this work, we address a challenge in video inpainting: reconstructing occluded regions in dynamic, real-world scenarios. Motivated by the need for continuous human motion monitoring in healthcare settings, where facial features are frequently obscured, we propose a diffusion-based video-level inpainting model, DiffMVR. Our approach introduces a dynamic dual-guided image prompting system, leveraging adaptive reference frames to guide the inpainting process. This enables the model to capture both fine-grained details and smooth transitions between video frames, offering precise control over inpainting direction and significantly improving restoration accuracy in challenging, dynamic environments. DiffMVR represents a significant advancement in the field of diffusion-based inpainting, with practical implications for real-time applications in various dynamic settings.

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Zheyan Zhang, Diego Klabjan, Renee CB Manworren. 2024-11-27. DiffMVR: Diffusion-based Automated Multi-Guidance Video Restoration. https://arxiv.org/abs/2411.18745

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