Video inpainting, or filling in missing parts of a video frame, is an important task for improving video quality. Recently, transformer-based methods have shown promising results in this area. However, these methods can be computationally expensive and may not always produce the best results. In this article, we introduce FlowLens, a new method that leverages clip-recurrent transformers to efficiently and accurately perform video inpainting.
FlowLens Overview
FlowLens is designed to address two main challenges in video inpainting: (1) the large amount of computation required for global information aggregation, and (2) the limited context provided by local temporal smoothness. To overcome these challenges, FlowLens employs a clip-recurrent transformer architecture that combines global and local information to generate high-quality inpainted frames.
Global Information Aggregation
In video inpainting, it is crucial to capture long-range dependencies between different parts of the frame. However, traditional methods rely on computing complex optical flows or using convolutional neural networks (CNNs) to extract features, which can be computationally expensive and may not always produce accurate results. To address this challenge, FlowLens uses a clip-recurrent transformer architecture that aggregates global information from different parts of the frame in an efficient manner.
Local Temporal Smoothness
Another challenge in video inpainting is ensuring that the inpainted frames are visually coherent and have good local temporal smoothness. However, traditional methods often struggle to capture this aspect, leading to noticeable artifacts or inconsistencies in the inpainted frames. FlowLens addresses this challenge by incorporating a hybrid propagation mechanism that combines the strengths of both global and local information aggregation.
Experiments
We evaluate FlowLens on several benchmark datasets, including YouTube-VOS, Cityscapes, and BSDS500. Our results show that FlowLens consistently outperforms state-of-the-art methods in terms of both qualitative and quantitative measures. Specifically, FlowLens achieves superior performance in terms of inpainting quality, temporal coherence, and accuracy in predicting high-level tasks such as object detection.
Conclusion
In summary, FlowLens is a novel method that leverages clip-recurrent transformers to efficiently and accurately perform video inpainting. By addressing the two main challenges of global information aggregation and local temporal smoothness, FlowLens produces high-quality inpainted frames with improved accuracy and efficiency compared to state-of-the-art methods. With its impressive performance and simple architecture, FlowLens has the potential to be a valuable tool for video inpainting applications.
