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

arXiv: https://arxiv.org/abs/2208.03742 · HTML: https://mlsanity.com/paper/2208.03742/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 34 references that are in the map.
- First posted on arXiv 7 Aug 2022.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [Implicit Neural Representations with Periodic Activation Functions](https://mlsanity.com/paper/2006.09661/) (2020)
- [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](https://mlsanity.com/paper/2006.10739/) (2020)
- [Occupancy Networks: Learning 3D Reconstruction in Function Space](https://mlsanity.com/paper/1812.03828/) (2018)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [On the Spectral Bias of Neural Networks](https://mlsanity.com/paper/1806.08734/) (2018)
- [Neural Sparse Voxel Fields](https://mlsanity.com/paper/2007.11571/) (2020)
- [Learning Continuous Image Representation with Local Implicit Image Function](https://mlsanity.com/paper/2012.09161/) (2020)
- [DVC: An End-to-end Deep Video Compression Framework](https://mlsanity.com/paper/1812.00101/) (2018)
- [NeRV: Neural Representations for Videos](https://mlsanity.com/paper/2110.13903/) (2021)
- [Learned Initializations for Optimizing Coordinate-Based Neural Representations](https://mlsanity.com/paper/2012.02189/) (2020)
- [Deep Contextual Video Compression](https://mlsanity.com/paper/2109.15047/) (2021)
- [FVC: A New Framework towards Deep Video Compression in Feature Space](https://mlsanity.com/paper/2105.09600/) (2021)
- [Video Compression through Image Interpolation](https://mlsanity.com/paper/1804.06919/) (2018)
- [Deep Video Inpainting](https://mlsanity.com/paper/1905.01639/) (2019)
- [FuseFormer: Fusing Fine-Grained Information in Transformers for Video Inpainting](https://mlsanity.com/paper/2109.02974/) (2021)
- [Learning for Video Compression with Hierarchical Quality and Recurrent Enhancement](https://mlsanity.com/paper/2003.01966/) (2020)
- [COCO-GAN: Generation by Parts via Conditional Coordinating](https://mlsanity.com/paper/1904.00284/) (2019)
- [Hierarchical Autoregressive Modeling for Neural Video Compression](https://mlsanity.com/paper/2010.10258/) (2020)
- [Efficient Video Compression via Content-Adaptive Super-Resolution](https://mlsanity.com/paper/2104.02322/) (2021)

## Built on by (most cited first, 30 of 32)

- [Implicit Neural Representation for Cooperative Low-light Image Enhancement](https://mlsanity.com/paper/2303.11722/) (2023)
- [HiNeRV: Video Compression with Hierarchical Encoding-based Neural Representation](https://mlsanity.com/paper/2306.09818/) (2023)
- [C3: High-performance and low-complexity neural compression from a single image or video](https://mlsanity.com/paper/2312.02753/) (2023)
- [NIRVANA: Neural Implicit Representations of Videos with Adaptive Networks and Autoregressive Patch-wise Modeling](https://mlsanity.com/paper/2212.14593/) (2022)
- [FFNeRV: Flow-Guided Frame-Wise Neural Representations for Videos](https://mlsanity.com/paper/2212.12294/) (2022)
- [USTC-TD: A Test Dataset and Benchmark for Image and Video Coding in 2020s](https://mlsanity.com/paper/2409.08481/) (2024)
- [Boosting Neural Representations for Videos with a Conditional Decoder](https://mlsanity.com/paper/2402.18152/) (2024)
- [NVRC: Neural Video Representation Compression](https://mlsanity.com/paper/2409.07414/) (2024)
- [DS-NeRV: Implicit Neural Video Representation with Decomposed Static and Dynamic Codes](https://mlsanity.com/paper/2403.15679/) (2024)
- [PNVC: Towards Practical INR-based Video Compression](https://mlsanity.com/paper/2409.00953/) (2024)
- [GIViC: Generative Implicit Video Compression](https://mlsanity.com/paper/2503.19604/) (2025)
- [Latent-INR: A Flexible Framework for Implicit Representations of Videos with Discriminative Semantics](https://mlsanity.com/paper/2408.02672/) (2024)
- [SNeRV: Spectra-preserving Neural Representation for Video](https://mlsanity.com/paper/2501.01681/) (2025)
- [Implicit-explicit Integrated Representations for Multi-view Video Compression](https://mlsanity.com/paper/2311.17350/) (2023)
- [RT-NeRV: Rethinking Hybrid Neural Representations for Video via Residual Tokenization](https://mlsanity.com/paper/2403.12401/) (2024)
- [Scene Matters: Model-based Deep Video Compression](https://mlsanity.com/paper/2303.04557/) (2023)
- [Ultra-lightweight Neural Video Representation Compression](https://mlsanity.com/paper/2512.04019/) (2025)
- [NERV++: An Enhanced Implicit Neural Video Representation](https://mlsanity.com/paper/2402.18305/) (2024)
- [MetaNeRV: Meta Neural Representations for Videos with Spatial-Temporal Guidance](https://mlsanity.com/paper/2501.02427/) (2025)
- [UAR-NVC: A Unified AutoRegressive Framework for Memory-Efficient Neural Video Compression](https://mlsanity.com/paper/2503.02733/) (2025)
- [High-Frequency Enhanced Hybrid Neural Representation for Video Compression](https://mlsanity.com/paper/2411.06685/) (2024)
- [CANeRV: Content Adaptive Neural Representation for Video Compression](https://mlsanity.com/paper/2502.06181/) (2025)
- [Tree-NeRV: A Tree-Structured Neural Representation for Efficient Non-Uniform Video Encoding](https://mlsanity.com/paper/2504.12899/) (2025)
- [Efficient Neural Video Representation with Temporally Coherent Modulation](https://mlsanity.com/paper/2505.00335/) (2025)
- [GSVR: 2D Gaussian-based Video Representation for 800+ FPS with Hybrid Deformation Field](https://mlsanity.com/paper/2507.05594/) (2025)
- [Range Image-Based Implicit Neural Compression for LiDAR Point Clouds](https://mlsanity.com/paper/2504.17229/) (2025)
- [CWRNN-INVR: A Coupled WarpRNN based Implicit Neural Video Representation](https://mlsanity.com/paper/2604.06564/) (2026)
- [MNeRV: A Multilayer Neural Representation for Videos](https://mlsanity.com/paper/2407.07347/) (2024)
- [Releasing the Parameter Latency of Neural Representation for High-Efficiency Video Compression](https://mlsanity.com/paper/2410.01654/) (2024)
- [Structure-Preserving Patch Decoding for Efficient Neural Video Representation](https://mlsanity.com/paper/2506.12896/) (2025)

## Abstract (arXiv)

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.