论文标题
学习高质量框架插值的跨效率神经表示
Learning Cross-Video Neural Representations for High-Quality Frame Interpolation
论文作者
论文摘要
本文考虑了时间视频插值的问题，其目标是在其两个邻居的情况下综合一个新的视频框架。我们建议基于神经场（NF）的第一种视频插值方法，提出了跨视频神经表示（治愈）。 NF是指最近在计算机视觉中获得广泛成功和应用的复杂3D场景神经表示的方法类别。 CURE将视频表示为通过基于坐标的神经网络参数化的连续函数，其输入是时空坐标，输出是相应的RGB值。 CURE引入了一种新的体系结构，该架构在输入帧上调节神经网络，以在合成视频中施加时空一致性。这不仅提高了最终的插值质量，而且还可以使治疗能够在多个视频中学习先验。实验评估表明，CURE可以在几个基准数据集上的视频插值上实现最新性能。
This paper considers the problem of temporal video interpolation, where the goal is to synthesize a new video frame given its two neighbors. We propose Cross-Video Neural Representation (CURE) as the first video interpolation method based on neural fields (NF). NF refers to the recent class of methods for the neural representation of complex 3D scenes that has seen widespread success and application across computer vision. CURE represents the video as a continuous function parameterized by a coordinate-based neural network, whose inputs are the spatiotemporal coordinates and outputs are the corresponding RGB values. CURE introduces a new architecture that conditions the neural network on the input frames for imposing space-time consistency in the synthesized video. This not only improves the final interpolation quality, but also enables CURE to learn a prior across multiple videos. Experimental evaluations show that CURE achieves the state-of-the-art performance on video interpolation on several benchmark datasets.
