# VORNet: Spatio-temporally Consistent Video Inpainting for Object Removal

arXiv: https://arxiv.org/abs/1904.06726 · HTML: https://mlsanity.com/paper/1904.06726/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Apr 2019.

## Built on by (most cited first, 11 of 11)

- [Explainability of deep vision-based autonomous driving systems: Review and challenges](https://mlsanity.com/paper/2101.05307/) (2021)
- [Deep Learning-based Image and Video Inpainting: A Survey](https://mlsanity.com/paper/2401.03395/) (2024)
- [Tell Me What Happened: Unifying Text-guided Video Completion via Multimodal Masked Video Generation](https://mlsanity.com/paper/2211.12824/) (2022)
- [EffectErase: Joint Video Object Removal and Insertion for High-Quality Effect Erasing](https://mlsanity.com/paper/2603.19224/) (2026)
- [M3L: Language-based Video Editing via Multi-Modal Multi-Level Transformers](https://mlsanity.com/paper/2104.01122/) (2021)
- [One-Shot Video Inpainting](https://mlsanity.com/paper/2302.14362/) (2023)
- [Spatial-Temporal Residual Aggregation for High Resolution Video Inpainting](https://mlsanity.com/paper/2111.03574/) (2021)
- [HySim: An Efficient Hybrid Similarity Measure for Patch Matching in Image Inpainting](https://mlsanity.com/paper/2403.14292/) (2024)
- [MagicRoad: Semantic-Aware 3D Road Surface Reconstruction via Obstacle Inpainting](https://mlsanity.com/paper/2507.23340/) (2025)
- [Transformer-Based Inpainting for Real-Time 3D Streaming in Sparse Multi-Camera Setups](https://mlsanity.com/paper/2603.05507/) (2026)
- [EffectLearner: World-Aware Object-Effect Reasoning for Real-World Video Object Removal](https://mlsanity.com/paper/2608.05565/) (2026)

## Abstract (arXiv)

Video object removal is a challenging task in video processing that often requires massive human efforts. Given the mask of the foreground object in each frame, the goal is to complete (inpaint) the object region and generate a video without the target object. While recently deep learning based methods have achieved great success on the image inpainting task, they often lead to inconsistent results between frames when applied to videos. In this work, we propose a novel learning-based Video Object Removal Network (VORNet) to solve the video object removal task in a spatio-temporally consistent manner, by combining the optical flow warping and image-based inpainting model. Experiments are done on our Synthesized Video Object Removal (SVOR) dataset based on the YouTube-VOS video segmentation dataset, and both the objective and subjective evaluation demonstrate that our VORNet generates more spatially and temporally consistent videos compared with existing methods.