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.
@article{arxiv.2411.18745,
title = {DiffMVR: Diffusion-based Automated Multi-Guidance Video Restoration},
author = {Zheyan Zhang and Diego Klabjan and Renee CB Manworren},
journal= {arXiv preprint arXiv:2411.18745},
year = {2024}
}