NeRV: Neural Representations for Videos
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Abstract (arXiv)
We propose a novel neural representation for videos (NeRV) which encodes videos in neural networks. Unlike conventional representations that treat videos as frame sequences, we represent videos as neural networks taking frame index as input. Given a frame index, NeRV outputs the corresponding RGB image. Video encoding in NeRV is simply fitting a neural network to video frames and decoding process is a simple feedforward operation. As an image-wise implicit representation, NeRV output the whole image and shows great efficiency compared to pixel-wise implicit representation, improving the encoding speed by 25x to 70x, the decoding speed by 38x to 132x, while achieving better video quality. With such a representation, we can treat videos as neural networks, simplifying several video-related tasks. For example, conventional video compression methods are restricted by a long and complex pipeline, specifically designed for the task. In contrast, with NeRV, we can use any neural network compression method as a proxy for video compression, and achieve comparable performance to traditional frame-based video compression approaches (H.264, HEVC etc). Besides compression, we demonstrate the generalization of NeRV for video denoising. The source code and pre-trained model can be found at https://github.com/haochen-rye/NeRV.git.
Key facts. Cited by 256 papers in the map (data through 14 Oct 2026). Builds on 44 of its 63 references that are in the map. First posted on arXiv 26 Oct 2021.
Builds on
44 of 63 references.
References · 44
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Citing papers per year: 2021: 3; 2022: 37; 2023: 44; 2024: 67; 2025: 60; 2026: 45.
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- 2022Is Attention All That NeRF Needs?cited by 113
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- 2023HNeRV: A Hybrid Neural Representation for Videoscited by 100
- 2024GaussianImage: 1000 FPS Image Representation and Compression by 2D Gaussian Splattingcited by 99
- 2023Implicit Neural Representation for Cooperative Low-light Image Enhancementcited by 92
- 2023Implicit Neural Representation in Medical Imaging: A Comparative Surveycited by 90
- 2022COIN++: Neural Compression Across Modalitiescited by 82
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- 2023FINER: Flexible spectral-bias tuning in Implicit NEural Representation by Variable-periodic Activation Functionscited by 78
- 2022E-NeRV: Expedite Neural Video Representation with Disentangled Spatial-Temporal Contextcited by 76
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- 2023HiNeRV: Video Compression with Hierarchical Encoding-based Neural Representationcited by 71
- 2022Unified Implicit Neural Stylizationcited by 67
- 2023NeuRBF: A Neural Fields Representation with Adaptive Radial Basis Functionscited by 65
- 2023Spatial Functa: Scaling Functa to ImageNet Classification and Generationcited by 57
- 2023C3: High-performance and low-complexity neural compression from a single image or videocited by 55
- 2024Bidirectional Multi-Scale Implicit Neural Representations for Image Derainingcited by 54
- 2021Implicit Neural Video Compressioncited by 52
- 2022NIRVANA: Neural Implicit Representations of Videos with Adaptive Networks and Autoregressive Patch-wise Modelingcited by 51
- 2022Signal Processing for Implicit Neural Representationscited by 46
- 2022FFNeRV: Flow-Guided Frame-Wise Neural Representations for Videoscited by 46
- 2023DNeRV: Modeling Inherent Dynamics via Difference Neural Representation for Videoscited by 42
- 2022Generalizable Implicit Neural Representations via Instance Pattern Composerscited by 39
- 2023Towards Scalable Neural Representation for Diverse Videoscited by 37
- 2024USTC-TD: A Test Dataset and Benchmark for Image and Video Coding in 2020scited by 37
- 2023PartNeRF: Generating Part-Aware Editable 3D Shapes without 3D Supervisioncited by 36
- 2022PS-NeRV: Patch-wise Stylized Neural Representations for Videoscited by 32
- 2022Compressing multidimensional weather and climate data into neural networkscited by 31
- 2023SHACIRA: Scalable HAsh-grid Compression for Implicit Neural Representationscited by 31
- 2022DINER: Disorder-Invariant Implicit Neural Representationcited by 30
- 2022Scalable Neural Video Representations with Learnable Positional Featurescited by 29
- 2024Boosting Neural Representations for Videos with a Conditional Decodercited by 29
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- 2023Disorder-invariant Implicit Neural Representationcited by 27
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- 2024DS-NeRV: Implicit Neural Video Representation with Decomposed Static and Dynamic Codescited by 25
- 2022Meta-Learning Sparse Compression Networkscited by 22
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- 2024PNVC: Towards Practical INR-based Video Compressioncited by 19
- 2025Instant GaussianImage: A Generalizable and Self-Adaptive Image Representation via 2D Gaussian Splattingcited by 18
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- 2026CoANeRV: Coordinate-Aware Token-Space Neural Video Representation14 Aug 2026
- 2026TOM-GS: Editable Video Representation via Temporal Opacity Modulation of Static 3D Gaussians21 Jul 2026
- 2026OmniStyle-INR: Universal and Multimodal Style Transfer for INRs17 Jul 2026
- 2026HyperGS: Fast and Generalizable Gaussian Video Representation13 Jul 2026
- 2026Knowledge as Orbit: Finite Collections as Phases of an Exactly Periodic Latent Generator11 Jul 2026
- 2026Data-driven Video Codec with Implicit Neural Representations2 Jul 2026
- 2026Enhanced Neural Video Representation Compression Across Extreme Complexity and Quality Scales26 Jun 2026
- 2026PaaF: Raising the perceived quality of INR-Based Image Compression19 Jun 2026
- 2026High-Fidelity Video Compression based on Invertible Neural Transform and Implicit Conditioning11 Jun 2026
- 2026From Content to Knowledge: Lightning Fast Long-Video Understanding with Neural Knowledge Representations10 Jun 2026
- 2026Ultra-Fast Neural Video Compression3 Jun 2026
- 2026NeR-SC: Adapting Neural Video Representation to Screen Content26 May 2026
- 2026What Cohort INRs Encode and Where to Freeze Them8 May 2026
- 2026Implicit Neural Representations: A Signal Processing Perspective16 Apr 2026
- 2026LumiMotion: Improving Gaussian Relighting with Scene Dynamics13 Apr 2026
- 2026STGV: Spatio-Temporal Hash Encoding for Gaussian-based Video Representation13 Apr 2026
- 2026TinyNeRV: Compact Neural Video Representations via Capacity Scaling, Distillation, and Low-Precision Inference10 Apr 2026
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- 2026TeCoNeRV: Leveraging Temporal Coherence for Compressible Neural Representations for Videos18 Feb 2026
- 2026HPC: Hierarchical Point-based Latent Representation for Streaming Dynamic Gaussian Splatting Compression31 Jan 2026
- 2026Implicit Neural Representation Facilitates Unified Universal Vision Encoding20 Jan 2026
- 2026HOSC: A Periodic Activation with Saturation Control for High-Fidelity Implicit Neural Representations10 Jan 2026
- 2026AdaGaR: Adaptive Gabor Representation for Dynamic Scene Reconstruction2 Jan 2026
- 2025Contour Information Aware 2D Gaussian Splatting for Image Representation29 Dec 2025
- 2025Towards Arbitrary Motion Completing via Hierarchical Continuous Representation24 Dec 2025
- 2025NeRV360: Neural Representation for 360-Degree Videos with a Viewport Decoder24 Dec 2025
- 2025Optimizing Rank for High-Fidelity Implicit Neural Representations16 Dec 2025
- 2025End-to-End Learning-based Video Streaming Enhancement Pipeline: A Generative AI Approach16 Dec 2025
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- 2026Resolution-Flexible Decoding for Hybrid Neural Video Representations20 Sep 2026
- 2026GenStream: Semantic Streaming Framework for Generative Reconstruction of Human-centric Media16 Sep 2026
- 2026CARA: Collision-Aware Resolution Adaptation for Multiresolution Hash Encoding Based Image Fitting16 Sep 2026
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- 2026Deformable 2D Gaussian Splatting for Efficient 4K Video Compression12 Sep 2026
- 2026Direct Topology Tracking in Continuous Implicit Models10 Sep 2026
- 2026PIC: Revisiting INR for Image Coding with Fast Encoding and Sub-Millisecond Decoding8 Sep 2026
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