用扩散模型修复动态视频中被遮挡的面部，提升医疗监控准确性
DiffMVR: Diffusion-based Automated Multi-Guidance Video Restoration
- 基于扩散模型，动态引用参考帧引导修复过程
- 在真实动态场景中实现高精度面部重建，细节与过渡更自然
- 适合医疗监控等实时动态视频修复场景
本文针对动态真实场景中视频修复的挑战，提出一种基于扩散模型的视频级修复方法DiffMVR。为应对医疗健康环境中频繁遮挡面部的需求，该方法引入动态双引导图像提示系统，利用自适应参考帧指导修复过程。模型能有效捕捉细粒度细节和帧间平滑过渡，精准控制修复方向，在复杂动态环境中显著提升修复准确率。DiffMVR在扩散模型视频修复领域取得重要进展，具备在多种动态场景中实时应用的潜力。
原文摘要 · Abstract (English)
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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