Existing implicit neural representation (INR) methods do not fully exploit
spatiotemporal redundancies in videos. Index-based INRs ignore the
content-specific spatial features and hybrid INRs ignore the contextual
dependency on adjacent frames, leading to poor modeling capability for scenes
with large motion or dynamics. We analyze this limitation from the perspective
of function fitting and reveal the importance of frame difference. To use
explicit motion information, we propose Difference Neural Representation for
Videos (DNeRV), which consists of two streams for content and frame difference.
We also introduce a collaborative content unit for effective feature fusion. We
test DNeRV for video compression, inpainting, and interpolation. DNeRV achieves
competitive results against the state-of-the-art neural compression approaches
and outperforms existing implicit methods on downstream inpainting and
interpolation for $960 \times 1920$ videos.

从函数拟合的角度分析了现有的隐式神经表征方法在处理具有大运动或动态场景时建模能力不足的问题，提出了基于帧间差异的 DNeRV 显式运动信息神经表征方法，用于视频压缩、修复和插帧等任务，并在 $960	imes1920$ 的视频上得到了比现有隐式方法更好的效果。