论文标题
视频框架插值的隐式神经表示的光流正规化
Optical Flow Regularization of Implicit Neural Representations for Video Frame Interpolation
论文作者
论文摘要
最近的工作表明，隐式神经表示（INR）具有信号导数的有意义表示的能力。在这项工作中，我们利用该属性来执行视频框架插值（VFI），通过明确限制INR的衍生物以满足光流约束方程。我们仅使用目标视频及其光流，在有限的运动范围内实现了最先进的VFI，而无需从其他培训数据中学习插值操作员。我们进一步表明，限制INR衍生物不仅可以更好地插值中间框架，而且还提高了狭窄网络符合观察到的帧的能力，这表明潜在的应用是进行视频压缩和INR优化的应用。
Recent works have shown the ability of Implicit Neural Representations (INR) to carry meaningful representations of signal derivatives. In this work, we leverage this property to perform Video Frame Interpolation (VFI) by explicitly constraining the derivatives of the INR to satisfy the optical flow constraint equation. We achieve state of the art VFI on limited motion ranges using only a target video and its optical flow, without learning the interpolation operator from additional training data. We further show that constraining the INR derivatives not only allows to better interpolate intermediate frames but also improves the ability of narrow networks to fit the observed frames, which suggests potential applications to video compression and INR optimization.
