Video holds significance in computer graphics applications. Because of the heterogeneous of digital devices, retargeting videos becomes an essential function to enhance user viewing experience in such applications. In the research of video retargeting, preserving the relevant visual content in videos, avoiding flicking, and processing time are the vital challenges. Extending image retargeting techniques to the video domain is challenging due to the high running time. Prior work of video retargeting mainly utilizes time-consuming preprocessing to analyze frames. Plus, being tolerant of different video content, avoiding important objects from shrinking, and the ability to play with arbitrary ratios are the limitations that need to be resolved in these systems requiring investigation. In this paper, we present an end-to-end RETVI method to retarget videos to arbitrary aspect ratios. We eliminate the computational bottleneck in the conventional approaches by designing RETVI with two modules, content feature analyzer (CFA) and adaptive deforming estimator (ADE). The extensive experiments and evaluations show that our system outperforms previous work in quality and running time. Visit our project website for more results at $\href{http://graphics.csie.ncku.edu.tw/RETVI}{http://graphics.csie.ncku.edu.tw/RETVI}$.

翻译：视频在计算机图形学应用中具有重要意义。由于数字设备的异构性，视频重定向成为增强此类应用中用户观看体验的关键功能。在视频重定向研究中，保留视频中的相关视觉内容、避免闪烁以及处理时间是重要挑战。由于运行时间长，将图像重定向技术扩展到视频领域具有挑战性。先前的视频重定向工作主要利用耗时的预处理来分析帧。此外，对不同视频内容的容忍性、避免重要对象缩小以及能够以任意比例播放，是这些系统需要解决的限制因素。本文提出了一种端到端的RETVI方法，可将视频重定向到任意宽高比。我们通过设计RETVI的两个模块——内容特征分析器和自适应变形估计器——消除了传统方法中的计算瓶颈。大量实验和评估表明，我们的系统在质量和运行时间上均优于先前工作。更多结果请访问我们的项目网站：$\href{http://graphics.csie.ncku.edu.tw/RETVI}{http://graphics.csie.ncku.edu.tw/RETVI}$。