SOTAVerified

Internal Video Inpainting by Implicit Long-range Propagation

2021-08-04ICCV 2021Code Available1· sign in to hype

Hao Ouyang, Tengfei Wang, Qifeng Chen

Code Available — Be the first to reproduce this paper.

Reproduce

Code

Abstract

We propose a novel framework for video inpainting by adopting an internal learning strategy. Unlike previous methods that use optical flow for cross-frame context propagation to inpaint unknown regions, we show that this can be achieved implicitly by fitting a convolutional neural network to known regions. Moreover, to handle challenging sequences with ambiguous backgrounds or long-term occlusion, we design two regularization terms to preserve high-frequency details and long-term temporal consistency. Extensive experiments on the DAVIS dataset demonstrate that the proposed method achieves state-of-the-art inpainting quality quantitatively and qualitatively. We further extend the proposed method to another challenging task: learning to remove an object from a video giving a single object mask in only one frame in a 4K video.

Tasks

Reproductions