# Deep Flow-Guided Video Inpainting

arXiv: https://arxiv.org/abs/1905.02884 · HTML: https://mlsanity.com/paper/1905.02884/

## Key facts

- Cited by 116 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2019.

## Built on by (most cited first, 30 of 116)

- [FlowFormer: A Transformer Architecture for Optical Flow](https://mlsanity.com/paper/2203.16194/) (2022)
- [ProPainter: Improving Propagation and Transformer for Video Inpainting](https://mlsanity.com/paper/2309.03897/) (2023)
- [SEA-RAFT: Simple, Efficient, Accurate RAFT for Optical Flow](https://mlsanity.com/paper/2405.14793/) (2024)
- [FlowFormer++: Masked Cost Volume Autoencoding for Pretraining Optical Flow Estimation](https://mlsanity.com/paper/2303.01237/) (2023)
- [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)
- [VideoFlow: Exploiting Temporal Cues for Multi-frame Optical Flow Estimation](https://mlsanity.com/paper/2303.08340/) (2023)
- [AVID: Any-Length Video Inpainting with Diffusion Model](https://mlsanity.com/paper/2312.03816/) (2023)
- [Removing Objects From Neural Radiance Fields](https://mlsanity.com/paper/2212.11966/) (2022)
- [High-fidelity 3D GAN Inversion by Pseudo-multi-view Optimization](https://mlsanity.com/paper/2211.15662/) (2022)
- [Flow-Guided Transformer for Video Inpainting](https://mlsanity.com/paper/2208.06768/) (2022)
- [InFusion: Inpainting 3D Gaussians via Learning Depth Completion from Diffusion Prior](https://mlsanity.com/paper/2404.11613/) (2024)
- [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)
- [DNeRV: Modeling Inherent Dynamics via Difference Neural Representation for Videos](https://mlsanity.com/paper/2304.06544/) (2023)
- [Progressive Temporal Feature Alignment Network for Video Inpainting](https://mlsanity.com/paper/2104.03507/) (2021)
- [Learning Optical Flow with Adaptive Graph Reasoning](https://mlsanity.com/paper/2202.03857/) (2022)
- [Tell Me What Happened: Unifying Text-guided Video Completion via Multimodal Masked Video Generation](https://mlsanity.com/paper/2211.12824/) (2022)
- [Bringing Old Films Back to Life](https://mlsanity.com/paper/2203.17276/) (2022)
- [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)
- [ClothFormer:Taming Video Virtual Try-on in All Module](https://mlsanity.com/paper/2204.12151/) (2022)
- [Learning Optical Flow from Event Camera with Rendered Dataset](https://mlsanity.com/paper/2303.11011/) (2023)
- [Learning Semantic-Aware Dynamics for Video Prediction](https://mlsanity.com/paper/2104.09762/) (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)
- [NTIRE 2026 Challenge on Bitstream-Corrupted Video Restoration: Methods and Results](https://mlsanity.com/paper/2604.06945/) (2026)
- [Bitstream-Corrupted Video Recovery: A Novel Benchmark Dataset and Method](https://mlsanity.com/paper/2309.13890/) (2023)

## Abstract (arXiv)

Video inpainting, which aims at filling in missing regions of a video, remains challenging due to the difficulty of preserving the precise spatial and temporal coherence of video contents. In this work we propose a novel flow-guided video inpainting approach. Rather than filling in the RGB pixels of each frame directly, we consider video inpainting as a pixel propagation problem. We first synthesize a spatially and temporally coherent optical flow field across video frames using a newly designed Deep Flow Completion network. Then the synthesized flow field is used to guide the propagation of pixels to fill up the missing regions in the video. Specifically, the Deep Flow Completion network follows a coarse-to-fine refinement to complete the flow fields, while their quality is further improved by hard flow example mining. Following the guide of the completed flow, the missing video regions can be filled up precisely. Our method is evaluated on DAVIS and YouTube-VOS datasets qualitatively and quantitatively, achieving the state-of-the-art performance in terms of inpainting quality and speed.