PS-NeRV: Patch-wise Stylized Neural Representations for Videos

TL;DR

PS-NeRV introduces a patch-wise implicit neural representation for videos that improves reconstruction quality and decoding speed, leveraging patch-based encoding with CNNs, MLPs, and AdaIN for enhanced high-frequency detail fitting.

Contribution

The paper proposes a novel patch-wise video representation method, PS-NeRV, combining CNNs, MLPs, and AdaIN to outperform pixel-wise and image-wise approaches.

Findings

Achieves high-quality video reconstruction with fast decoding.

Effective in video compression and inpainting tasks.

Outperforms existing implicit neural representation methods.

Abstract

We study how to represent a video with implicit neural representations (INRs). Classical INRs methods generally utilize MLPs to map input coordinates to output pixels. While some recent works have tried to directly reconstruct the whole image with CNNs. However, we argue that both the above pixel-wise and image-wise strategies are not favorable to video data. Instead, we propose a patch-wise solution, PS-NeRV, which represents videos as a function of patches and the corresponding patch coordinate. It naturally inherits the advantages of image-wise methods, and achieves excellent reconstruction performance with fast decoding speed. The whole method includes conventional modules, like positional embedding, MLPs and CNNs, while also introduces AdaIN to enhance intermediate features. These simple yet essential changes could help the network easily fit high-frequency details. Extensive experiments have demonstrated its effectiveness in several video-related tasks, such as video compression and video inpainting.