DiffuEraser: A Diffusion Model for Video Inpainting
Authors: name: Xiaowen Li, name: Haolan Xue, name: Peiran Ren, name: Liefeng Bo

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.