Large motion poses a critical challenge in Video Frame Interpolation (VFI)
task. Existing methods are often constrained by limited receptive fields,
resulting in sub-optimal performance when handling scenarios with large motion.
In this paper, we introduce a new pipeline for VFI, which can effectively
integrate global-level information to alleviate issues associated with large
motion. Specifically, we first estimate a pair of initial intermediate flows
using a high-resolution feature map for extracting local details. Then, we
incorporate a sparse global matching branch to compensate for flow estimation,
which consists of identifying flaws in initial flows and generating sparse flow
compensation with a global receptive field. Finally, we adaptively merge the
initial flow estimation with global flow compensation, yielding a more accurate
intermediate flow. To evaluate the effectiveness of our method in handling
large motion, we carefully curate a more challenging subset from commonly used
benchmarks. Our method demonstrates the state-of-the-art performance on these
VFI subsets with large motion.

通过引入一个新的管道，本文提出了一种有效地整合全局级信息以缓解大运动问题的视频帧插值方法，在处理大运动场景时展现了最先进的性能。

视频帧插值中的稀疏全局匹配与大动态

Sparse Global Matching for Video Frame Interpolation with Large Motion

Video super-resolution (VSR) aims at restoring a video in low-resolution (LR)
and improving it to higher-resolution (HR). Due to the characteristics of video
tasks, it is very important that motion information among frames should be well
concerned, summarized and utilized for guidance in a VSR algorithm. Especially,
when a video contains large motion, conventional methods easily bring
incoherent results or artifacts. In this paper, we propose a novel deep neural
network with Dual Subnet and Multi-stage Communicated Upsampling (DSMC) for
super-resolution of videos with large motion. We design a new module named
U-shaped residual dense network with 3D convolution (U3D-RDN) for fine implicit
motion estimation and motion compensation (MEMC) as well as coarse spatial
feature extraction. And we present a new Multi-Stage Communicated Upsampling
(MSCU) module to make full use of the intermediate results of upsampling for
guiding the VSR. Moreover, a novel dual subnet is devised to aid the training
of our DSMC, whose dual loss helps to reduce the solution space as well as
enhance the generalization ability. Our experimental results confirm that our
method achieves superior performance on videos with large motion compared to
state-of-the-art methods.

本文提出了一种新型的深度学习神经网络算法，在视频超分辨重建中优化运动信息处理和利用，解决了普通算法在处理大运动时可能出现的不连贯结果或者伪影问题，同时改良了局部空间特征提取和显式运动估计技术，获得了比现有方法更优秀的效果。

双子网和多级通讯上采样的大运动视频超分辨率

Large Motion Video Super-Resolution with Dual Subnet and Multi-Stage  Communicated Upsampling

It has been recently shown that a convolutional neural network can learn
optical flow estimation with unsupervised learning. However, the performance of
the unsupervised methods still has a relatively large gap compared to its
supervised counterpart. Occlusion and large motion are some of the major
factors that limit the current unsupervised learning of optical flow methods.
In this work we introduce a new method which models occlusion explicitly and a
new warping way that facilitates the learning of large motion. Our method shows
promising results on Flying Chairs, MPI-Sintel and KITTI benchmark datasets.
Especially on KITTI dataset where abundant unlabeled samples exist, our
unsupervised method outperforms its counterpart trained with supervised
learning.

本文研究了卷积神经网络在无监督学习下学习光流预测的能力；提出了一种新的模型来显式模拟遮挡问题，并使用新颖的变换方式来更好地学习大运动；在 Flying Chairs，MPI-Sintel 和 KITTI 标准数据集上进行了测试，结果表明在 KITTI 数据集上无监督方法的效果优于有监督方法，尤其在该数据集上，我们的方法表现出色。