# Video Inpainting of Complex Scenes

arXiv: https://arxiv.org/abs/1503.05528 · HTML: https://mlsanity.com/paper/1503.05528/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2015.

## Built on by (most cited first, 30 of 41)

- [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)
- [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)
- [Omnimatte: Associating Objects and Their Effects in Video](https://mlsanity.com/paper/2105.06993/) (2021)
- [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)
- [Progressive Temporal Feature Alignment Network for Video Inpainting](https://mlsanity.com/paper/2104.03507/) (2021)
- [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)
- [Internal Video Inpainting by Implicit Long-range Propagation](https://mlsanity.com/paper/2108.01912/) (2021)
- [Bitstream-Corrupted Video Recovery: A Novel Benchmark Dataset and Method](https://mlsanity.com/paper/2309.13890/) (2023)
- [Diverse Generation from a Single Video Made Possible](https://mlsanity.com/paper/2109.08591/) (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)
- [Flow-Guided Diffusion for Video Inpainting](https://mlsanity.com/paper/2311.15368/) (2023)
- [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)
- [Exploiting Optical Flow Guidance for Transformer-Based Video Inpainting](https://mlsanity.com/paper/2301.10048/) (2023)
- [Coherent Video Inpainting Using Optical Flow-Guided Efficient Diffusion](https://mlsanity.com/paper/2412.00857/) (2024)
- [Object-WIPER : Training-Free Object and Associated Effect Removal in Videos](https://mlsanity.com/paper/2601.06391/) (2026)
- [Beyond the Field-of-View: Enhancing Scene Visibility and Perception with Clip-Recurrent Transformer](https://mlsanity.com/paper/2211.11293/) (2022)
- [Towards Online Real-Time Memory-based Video Inpainting Transformers](https://mlsanity.com/paper/2403.16161/) (2024)
- [High-fidelity 3D Gaussian Inpainting: preserving multi-view consistency and photorealistic details](https://mlsanity.com/paper/2507.18023/) (2025)
- [Restore from Restored: Single-image Inpainting](https://mlsanity.com/paper/2110.12822/) (2021)
- [Spatial-Temporal Residual Aggregation for High Resolution Video Inpainting](https://mlsanity.com/paper/2111.03574/) (2021)
- [Deep image prior inpainting of ancient frescoes in the Mediterranean Alpine arc](https://mlsanity.com/paper/2306.14209/) (2023)
- [Depth-Aware Endoscopic Video Inpainting](https://mlsanity.com/paper/2407.02675/) (2024)

## Abstract (arXiv)

We propose an automatic video inpainting algorithm which relies on the optimisation of a global, patch-based functional. Our algorithm is able to deal with a variety of challenging situations which naturally arise in video inpainting, such as the correct reconstruction of dynamic textures, multiple moving objects and moving background. Furthermore, we achieve this in an order of magnitude less execution time with respect to the state-of-the-art. We are also able to achieve good quality results on high definition videos. Finally, we provide specific algorithmic details to make implementation of our algorithm as easy as possible. The resulting algorithm requires no segmentation or manual input other than the definition of the inpainting mask, and can deal with a wider variety of situations than is handled by previous work. 1. Introduction. Advanced image and video editing techniques are increasingly common in the image processing and computer vision world, and are also starting to be used in media entertainment. One common and difficult task closely linked to the world of video editing is image and video " inpainting ". Generally speaking, this is the task of replacing the content of an image or video with some other content which is visually pleasing. This subject has been extensively studied in the case of images, to such an extent that commercial image inpainting products destined for the general public are available, such as Photoshop's " Content Aware fill " [1]. However, while some impressive results have been obtained in the case of videos, the subject has been studied far less extensively than image inpainting. This relative lack of research can largely be attributed to high time complexity due to the added temporal dimension. Indeed, it has only very recently become possible to produce good quality inpainting results on high definition videos, and this only in a semi-automatic manner. Nevertheless, high-quality video inpainting has many important and useful applications such as film restoration, professional post-production in cinema and video editing for personal use. For this reason, we believe that an automatic, generic video inpainting algorithm would be extremely useful for both academic and professional communities.