# Short-Term and Long-Term Context Aggregation Network for Video Inpainting

arXiv: https://arxiv.org/abs/2009.05721 · HTML: https://mlsanity.com/paper/2009.05721/

## Key facts

- Cited by 34 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 12 Sep 2020.

## Built on by (most cited first, 30 of 34)

- [ProPainter: Improving Propagation and Transformer for Video Inpainting](https://mlsanity.com/paper/2309.03897/) (2023)
- [Backdoor Defense via Decoupling the Training Process](https://mlsanity.com/paper/2202.03423/) (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)
- [Deep Learning-based Image and Video Inpainting: A Survey](https://mlsanity.com/paper/2401.03395/) (2024)
- [Progressive Temporal Feature Alignment Network for Video Inpainting](https://mlsanity.com/paper/2104.03507/) (2021)
- [Towards Language-Driven Video Inpainting via Multimodal Large Language Models](https://mlsanity.com/paper/2401.10226/) (2024)
- [ROSE: Remove Objects with Side Effects in Videos](https://mlsanity.com/paper/2508.18633/) (2025)
- [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)
- [Internal Video Inpainting by Implicit Long-range Propagation](https://mlsanity.com/paper/2108.01912/) (2021)
- [Flow-Guided Video Inpainting with Scene Templates](https://mlsanity.com/paper/2108.12845/) (2021)
- [DeViT: Deformed Vision Transformers in Video Inpainting](https://mlsanity.com/paper/2209.13925/) (2022)
- [TACO: Taming Diffusion for in-the-wild Video Amodal Completion](https://mlsanity.com/paper/2503.12049/) (2025)
- [PISCO: Precise Video Instance Insertion with Sparse Control](https://mlsanity.com/paper/2602.08277/) (2026)
- [Semi-Supervised Video Inpainting with Cycle Consistency Constraints](https://mlsanity.com/paper/2208.06807/) (2022)
- [Deep Face Video Inpainting via UV Mapping](https://mlsanity.com/paper/2109.00681/) (2021)
- [Feature-Aligned Video Raindrop Removal with Temporal Constraints](https://mlsanity.com/paper/2205.14574/) (2022)
- [Exploiting Optical Flow Guidance for Transformer-Based Video Inpainting](https://mlsanity.com/paper/2301.10048/) (2023)
- [Towards Understanding How Self-training Tolerates Data Backdoor Poisoning](https://mlsanity.com/paper/2301.08751/) (2023)
- [One-Shot Video Inpainting](https://mlsanity.com/paper/2302.14362/) (2023)
- [Towards Online Real-Time Memory-based Video Inpainting Transformers](https://mlsanity.com/paper/2403.16161/) (2024)
- [BVINet: Unlocking Blind Video Inpainting with Zero Annotations](https://mlsanity.com/paper/2502.01181/) (2025)
- [Spatial-Temporal Residual Aggregation for High Resolution Video Inpainting](https://mlsanity.com/paper/2111.03574/) (2021)
- [Raformer: Redundancy-Aware Transformer for Video Wire Inpainting](https://mlsanity.com/paper/2404.15802/) (2024)
- [VIP: Video Inpainting Pipeline for Real World Human Removal](https://mlsanity.com/paper/2504.03041/) (2025)
- [Learning Stochastic Bridges for Video Object Removal via Video-to-Video Translation](https://mlsanity.com/paper/2601.12066/) (2026)
- [From Understanding to Erasing: Towards Complete and Stable Video Object Removal](https://mlsanity.com/paper/2604.01693/) (2026)

## Abstract (arXiv)

Video inpainting aims to restore missing regions of a video and has many applications such as video editing and object removal. However, existing methods either suffer from inaccurate short-term context aggregation or rarely explore long-term frame information. In this work, we present a novel context aggregation network to effectively exploit both short-term and long-term frame information for video inpainting. In the encoding stage, we propose boundary-aware short-term context aggregation, which aligns and aggregates, from neighbor frames, local regions that are closely related to the boundary context of missing regions into the target frame. Furthermore, we propose dynamic long-term context aggregation to globally refine the feature map generated in the encoding stage using long-term frame features, which are dynamically updated throughout the inpainting process. Experiments show that it outperforms state-of-the-art methods with better inpainting results and fast inpainting speed.