# Copy-and-Paste Networks for Deep Video Inpainting

arXiv: https://arxiv.org/abs/1908.11587 · HTML: https://mlsanity.com/paper/1908.11587/

## Key facts

- Cited by 58 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2019.

## Built on by (most cited first, 30 of 58)

- [ProPainter: Improving Propagation and Transformer for Video Inpainting](https://mlsanity.com/paper/2309.03897/) (2023)
- [Human-to-Robot Imitation in the Wild](https://mlsanity.com/paper/2207.09450/) (2022)
- [Towards An End-to-End Framework for Flow-Guided Video Inpainting](https://mlsanity.com/paper/2204.02663/) (2022)
- [FuseFormer: Fusing Fine-Grained Information in Transformers for Video Inpainting](https://mlsanity.com/paper/2109.02974/) (2021)
- [VideoPainter: Any-length Video Inpainting and Editing with Plug-and-Play Context Control](https://mlsanity.com/paper/2503.05639/) (2025)
- [Flow-Guided Transformer for Video Inpainting](https://mlsanity.com/paper/2208.06768/) (2022)
- [TransFill: Reference-guided Image Inpainting by Merging Multiple Color and Spatial Transformations](https://mlsanity.com/paper/2103.15982/) (2021)
- [Deep Learning-based Image and Video Inpainting: A Survey](https://mlsanity.com/paper/2401.03395/) (2024)
- [ROSE: Remove Objects with Side Effects in Videos](https://mlsanity.com/paper/2508.18633/) (2025)
- [Towards Language-Driven Video Inpainting via Multimodal Large Language Models](https://mlsanity.com/paper/2401.10226/) (2024)
- [Occlusion-Aware Video Object Inpainting](https://mlsanity.com/paper/2108.06765/) (2021)
- [Decoupled Spatial-Temporal Transformer for Video Inpainting](https://mlsanity.com/paper/2104.06637/) (2021)
- [Look Ma, No Hands! Agent-Environment Factorization of Egocentric Videos](https://mlsanity.com/paper/2305.16301/) (2023)
- [Error Compensation Framework for Flow-Guided Video Inpainting](https://mlsanity.com/paper/2207.10391/) (2022)
- [Internal Video Inpainting by Implicit Long-range Propagation](https://mlsanity.com/paper/2108.01912/) (2021)
- [DeViT: Deformed Vision Transformers in Video Inpainting](https://mlsanity.com/paper/2209.13925/) (2022)
- [The DEVIL is in the Details: A Diagnostic Evaluation Benchmark for Video Inpainting](https://mlsanity.com/paper/2105.05332/) (2021)
- [Flow-Guided Video Inpainting with Scene Templates](https://mlsanity.com/paper/2108.12845/) (2021)
- [Deep Video Inpainting Detection](https://mlsanity.com/paper/2101.11080/) (2021)
- [VIEW: Visual Imitation Learning with Waypoints](https://mlsanity.com/paper/2404.17906/) (2024)
- [TACO: Taming Diffusion for in-the-wild Video Amodal Completion](https://mlsanity.com/paper/2503.12049/) (2025)
- [Semi-Supervised Video Inpainting with Cycle Consistency Constraints](https://mlsanity.com/paper/2208.06807/) (2022)
- [Semantically Consistent Video Inpainting with Conditional Diffusion Models](https://mlsanity.com/paper/2405.00251/) (2024)
- [Deep Face Video Inpainting via UV Mapping](https://mlsanity.com/paper/2109.00681/) (2021)
- [A Review on Visual Privacy Preservation Techniques for Active and Assisted Living](https://mlsanity.com/paper/2112.09422/) (2021)
- [Exploiting Optical Flow Guidance for Transformer-Based Video Inpainting](https://mlsanity.com/paper/2301.10048/) (2023)
- [Beyond the Field-of-View: Enhancing Scene Visibility and Perception with Clip-Recurrent Transformer](https://mlsanity.com/paper/2211.11293/) (2022)
- [Deficiency-Aware Masked Transformer for Video Inpainting](https://mlsanity.com/paper/2307.08629/) (2023)
- [BVINet: Unlocking Blind Video Inpainting with Zero Annotations](https://mlsanity.com/paper/2502.01181/) (2025)
- [Temporal View Synthesis of Dynamic Scenes through 3D Object Motion Estimation with Multi-Plane Images](https://mlsanity.com/paper/2208.09463/) (2022)

## Abstract (arXiv)

We present a novel deep learning based algorithm for video inpainting. Video inpainting is a process of completing corrupted or missing regions in videos. Video inpainting has additional challenges compared to image inpainting due to the extra temporal information as well as the need for maintaining the temporal coherency. We propose a novel DNN-based framework called the Copy-and-Paste Networks for video inpainting that takes advantage of additional information in other frames of the video. The network is trained to copy corresponding contents in reference frames and paste them to fill the holes in the target frame. Our network also includes an alignment network that computes affine matrices between frames for the alignment, enabling the network to take information from more distant frames for robustness. Our method produces visually pleasing and temporally coherent results while running faster than the state-of-the-art optimization-based method. In addition, we extend our framework for enhancing over/under exposed frames in videos. Using this enhancement technique, we were able to significantly improve the lane detection accuracy on road videos.