论文标题
部分可观测时空混沌系统的无模型预测
A Unified Pyramid Recurrent Network for Video Frame Interpolation
论文作者
论文摘要
流引导的合成为框架插值提供了一个共同的框架，其中估计光流以指导连续输入之间的中间帧的合成。在本文中，我们提出了UPR-NET，这是一种新型的统一金字塔复发网络，用于框架插值。在柔性金字塔框架中铸造，UPR-NET利用双向流量估计和中间框架合成的轻质复发模块。在每个金字塔水平上，它利用估计的双向流以生成框架合成的前进表示。在金字塔水平上，它可以为光流和中间框架迭代精致。特别是，我们表明我们的迭代合成策略可以显着改善大型运动案例框架插值的鲁棒性。尽管非常轻巧（参数为170万），我们的UPR-NET的基本版本在各种基准测试方面取得了出色的性能。我们的UPR-NET系列的代码和训练有素的模型可在以下网址找到：https：//github.com/srcn-ivl/upr-net。
Flow-guided synthesis provides a common framework for frame interpolation, where optical flow is estimated to guide the synthesis of intermediate frames between consecutive inputs. In this paper, we present UPR-Net, a novel Unified Pyramid Recurrent Network for frame interpolation. Cast in a flexible pyramid framework, UPR-Net exploits lightweight recurrent modules for both bi-directional flow estimation and intermediate frame synthesis. At each pyramid level, it leverages estimated bi-directional flow to generate forward-warped representations for frame synthesis; across pyramid levels, it enables iterative refinement for both optical flow and intermediate frame. In particular, we show that our iterative synthesis strategy can significantly improve the robustness of frame interpolation on large motion cases. Despite being extremely lightweight (1.7M parameters), our base version of UPR-Net achieves excellent performance on a large range of benchmarks. Code and trained models of our UPR-Net series are available at: https://github.com/srcn-ivl/UPR-Net.
