With the advent of Neural Style Transfer (NST), stylizing an image has become quite popular. A convenient way for extending stylization techniques to videos is by applying them on a per-frame basis. However, such per-frame application usually lacks temporal-consistency expressed by undesirable flickering artifacts. Most of the existing approaches for enforcing temporal-consistency suffers from one or more of the following drawbacks. They (1) are only suitable for a limited range of stylization techniques, (2) can only be applied in an offline fashion requiring the complete video as input, (3) cannot provide consistency for the task of stylization, or (4) do not provide interactive consistency-control. Note that existing consistent video-filtering approaches aim to completely remove flickering artifacts and thus do not respect any specific consistency-control aspect. For stylization tasks, however, consistency-control is an essential requirement where a certain amount of flickering can add to the artistic look and feel. Moreover, making this control interactive is paramount from a usability perspective. To achieve the above requirements, we propose an approach that can stylize video streams while providing interactive consistency-control. Apart from stylization, our approach also supports various other image processing filters. For achieving interactive performance, we develop a lite optical-flow network that operates at 80 Frames per second (FPS) on desktop systems with sufficient accuracy. We show that the final consistent video-output using our flow network is comparable to that being obtained using state-of-the-art optical-flow network. Further, we employ an adaptive combination of local and global consistent features and enable interactive selection between the two. By objective and subjective evaluation, we show that our method is superior to state-of-the-art approaches.
翻译:随着神经风格传输(NST)的到来,将图像系统化变得相当受欢迎。向视频系统化技术推广的方便方式是按每个框架来应用这些技术。然而,这种每框架应用通常缺乏不受欢迎的闪烁艺术品所表现的时间一致性。执行时间一致性的现有方法大多存在以下一个或多个缺陷。它们(1) 仅适合有限的系统化技术,(2) 只能以离线方式应用,需要完整的视频作为投入,(3) 无法为视频任务提供一致性,或者(4) 不提供交互式一致性控制。注意到现有的连续的视频过滤方法旨在完全清除闪烁的文物,因此不尊重任何具体的一致性控制方面。但是,对于调动性任务来说,一致性控制是一项基本要求,其中一定的闪烁能够增加艺术的外观和感觉。此外,从可用性角度来说,使这种控制互动至关重要。为了实现上述要求,我们建议一种可比较的视频流流流流方法,即:使用双向的视频流流流,可以使用双向的图像流,同时通过互动的图像网络来显示我们不断的流。我们通过持续流来展示另一个图像控制。我们不断的图像网络系统化,我们通过互动的流来显示另一个的流程。我们通过持续的图像流来显示我们实现。