DiffMVR: Diffusion-based Automated Multi-Guidance Video Restoration
This work addresses video inpainting for healthcare applications where facial features are obscured, but it appears incremental as it builds on existing diffusion-based methods with a new guidance system.
The paper tackled the challenge of reconstructing occluded regions in dynamic video inpainting, particularly for healthcare monitoring, and proposed DiffMVR, a diffusion-based model that improves restoration accuracy in such environments.
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.