DiffuEraser: A Diffusion Model for Video Inpainting

TL;DR

DiffuEraser introduces a diffusion-based video inpainting model that enhances detail, structure, and temporal consistency in masked regions, outperforming existing methods especially with large masks.

Contribution

The paper presents DiffuEraser, a novel diffusion model for video inpainting that leverages prior information and expanded temporal receptive fields for improved results.

Findings

Outperforms state-of-the-art in content completeness

Achieves superior temporal consistency

Maintains acceptable efficiency

Abstract

Recent video inpainting algorithms integrate flow-based pixel propagation with transformer-based generation to leverage optical flow for restoring textures and objects using information from neighboring frames, while completing masked regions through visual Transformers. However, these approaches often encounter blurring and temporal inconsistencies when dealing with large masks, highlighting the need for models with enhanced generative capabilities. Recently, diffusion models have emerged as a prominent technique in image and video generation due to their impressive performance. In this paper, we introduce DiffuEraser, a video inpainting model based on stable diffusion, designed to fill masked regions with greater details and more coherent structures. We incorporate prior information to provide initialization and weak conditioning,which helps mitigate noisy artifacts and suppress hallucinations. Additionally, to improve temporal consistency during long-sequence inference, we expand the temporal receptive fields of both the prior model and DiffuEraser, and further enhance consistency by leveraging the temporal smoothing property of Video Diffusion Models. Experimental results demonstrate that our proposed method outperforms state-of-the-art techniques in both content completeness and temporal consistency while maintaining acceptable efficiency.