论文标题
JNMR：视频框架插值的联合非线性运动回归
JNMR: Joint Non-linear Motion Regression for Video Frame Interpolation
论文作者
论文摘要
视频框架插值（VFI）旨在通过从双向历史参考文献中扭曲可学习的动作来产生预测框架。大多数现有作品都利用时空语义信息提取器来实现运动估计和插值建模。但是，他们不足以考虑产生的中间动作的真正机械理性。在本文中，我们将VFI重新制定为联合非线性运动回归（JNMR）策略，以模拟框架间的复杂运动。具体而言，目标框架和多个参考帧之间的运动轨迹通过多阶段二次模型的时间串联进行回归。 Convlstm被采用在时间维度中构建完整动作的联合分布。此外，功能学习网络旨在为联合回归建模进行优化。还进行了一个粗到精细的合成增强模块，以通过重复回归和插值来学习不同分辨率的视觉动力学。 VFI的实验结果表明，与最先进的方法相比，关节运动回归的有效性和显着改善。该代码可在https://github.com/ruhig6/jnmr上找到。
Video frame interpolation (VFI) aims to generate predictive frames by warping learnable motions from the bidirectional historical references. Most existing works utilize spatio-temporal semantic information extractor to realize motion estimation and interpolation modeling. However, they insufficiently consider the real mechanistic rationality of generated middle motions. In this paper, we reformulate VFI as a Joint Non-linear Motion Regression (JNMR) strategy to model the complicated motions of inter-frame. Specifically, the motion trajectory between the target frame and the multiple reference frames is regressed by a temporal concatenation of multi-stage quadratic models. ConvLSTM is adopted to construct this joint distribution of complete motions in temporal dimension. Moreover, the feature learning network is designed to optimize for the joint regression modeling. A coarse-to-fine synthesis enhancement module is also conducted to learn visual dynamics at different resolutions through repetitive regression and interpolation. Experimental results on VFI show that the effectiveness and significant improvement of joint motion regression compared with the state-of-the-art methods. The code is available at https://github.com/ruhig6/JNMR.
