论文标题
视频插值通过事件驱动的各向异性调节光流量调整
Video Interpolation by Event-driven Anisotropic Adjustment of Optical Flow
论文作者
论文摘要
视频框架插值是一项艰巨的任务，这是由于不断变化的现实场景。先前的方法通常计算双向光学流，然后在线性运动假设下预测中间光流，从而导致各向同性中间流量产生。随访研究通过估计的高阶运动信息和额外的帧获得各向异性调整。基于运动假设，它们的方法很难在真实场景中对复杂的运动进行建模。在本文中，我们提出了一种端到端训练方法A^2OF，用于视频框架插值，并通过事件驱动的各向异性调整光学流量调整。具体而言，我们使用事件为中间光流生成光流分布面具，这可以对两个帧之间的复杂运动进行建模。我们所提出的方法在视频框架插值中优于先前的方法，将基于事件的视频插值带到了更高的阶段。
Video frame interpolation is a challenging task due to the ever-changing real-world scene. Previous methods often calculate the bi-directional optical flows and then predict the intermediate optical flows under the linear motion assumptions, leading to isotropic intermediate flow generation. Follow-up research obtained anisotropic adjustment through estimated higher-order motion information with extra frames. Based on the motion assumptions, their methods are hard to model the complicated motion in real scenes. In this paper, we propose an end-to-end training method A^2OF for video frame interpolation with event-driven Anisotropic Adjustment of Optical Flows. Specifically, we use events to generate optical flow distribution masks for the intermediate optical flow, which can model the complicated motion between two frames. Our proposed method outperforms the previous methods in video frame interpolation, taking supervised event-based video interpolation to a higher stage.
