mlsanityData to 14 Oct 2026

VideoINR: Learning Video Implicit Neural Representation for Continuous Space-Time Super-Resolution

arXiv 2206.04647 ↗First posted 9 Jun 2022eess.IV cs.CV cs.LGcited by 101

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Abstract (arXiv)

Videos typically record the streaming and continuous visual data as discrete consecutive frames. Since the storage cost is expensive for videos of high fidelity, most of them are stored in a relatively low resolution and frame rate. Recent works of Space-Time Video Super-Resolution (STVSR) are developed to incorporate temporal interpolation and spatial super-resolution in a unified framework. However, most of them only support a fixed up-sampling scale, which limits their flexibility and applications. In this work, instead of following the discrete representations, we propose Video Implicit Neural Representation (VideoINR), and we show its applications for STVSR. The learned implicit neural representation can be decoded to videos of arbitrary spatial resolution and frame rate. We show that VideoINR achieves competitive performances with state-of-the-art STVSR methods on common up-sampling scales and significantly outperforms prior works on continuous and out-of-training-distribution scales. Our project page is at http://zeyuan-chen.com/VideoINR/ .

Key facts. Cited by 101 papers in the map (data through 14 Oct 2026). Builds on 42 of its 55 references that are in the map. First posted on arXiv 9 Jun 2022.

Builds on

42 of 55 references.

References · 42

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2017Quo Vadis, Action Recognition? A New Model and the Kinetics Datasetcited by 3,270
  3. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,689
  4. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,238
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  12. 2020pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesiscited by 708
30 more
  1. 2020GRAF: Generative Radiance Fields for 3D-Aware Image Synthesiscited by 689
  2. 2018Deformable ConvNets v2: More Deformable, Better Resultscited by 670
  3. 2017Video Enhancement with Task-Oriented Flowcited by 619
  4. 2020Learning Continuous Image Representation with Local Implicit Image Functioncited by 552
  5. 2019EDVR: Video Restoration with Enhanced Deformable Convolutional Networkscited by 494
  6. 2017Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolutioncited by 453
  7. 2017Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolationcited by 326
  8. 2020BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyondcited by 304
  9. 2020Softmax Splatting for Video Frame Interpolationcited by 279
  10. 2019Learning Shape Templates with Structured Implicit Functionscited by 242
  11. 2019Depth-Aware Video Frame Interpolationcited by 234
  12. 2018TDAN: Temporally Deformable Alignment Network for Video Super-Resolutioncited by 226
  13. 2017Video Frame Interpolation via Adaptive Separable Convolutioncited by 213
  14. 2017Flow-Guided Feature Aggregation for Video Object Detectioncited by 177
  15. 2017Video Frame Interpolation via Adaptive Convolutioncited by 158
  16. 2016Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensationcited by 156
  17. 2018Context-aware Synthesis for Video Frame Interpolationcited by 153
  18. 2019NASA: Neural Articulated Shape Approximationcited by 149
  19. 2017Detail-revealing Deep Video Super-resolutioncited by 148
  20. 2019Quadratic video interpolationcited by 145
  21. 2020Adversarial Generation of Continuous Imagescited by 135
  22. 2021Unconstrained Scene Generation with Locally Conditioned Radiance Fieldscited by 130
  23. 2020Video Super-Resolution with Recurrent Structure-Detail Networkcited by 116
  24. 2020Image Generators with Conditionally-Independent Pixel Synthesiscited by 106
  25. 2020Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolutioncited by 91
  26. 2021UltraSR: Spatial Encoding is a Missing Key for Implicit Image Function-based Arbitrary-Scale Super-Resolutioncited by 57
  27. 2021Temporal Modulation Network for Controllable Space-Time Video Super-Resolutioncited by 54
  28. 2020Space-Time-Aware Multi-Resolution Video Enhancementcited by 49
  29. 2019Unsupervised Video Interpolation Using Cycle Consistencycited by 44
  30. 2019FISR: Deep Joint Frame Interpolation and Super-Resolution with a Multi-scale Temporal Losscited by 28

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 5; 2023: 21; 2024: 29; 2025: 33; 2026: 13.

101 papers.

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  1. 2023HiNeRV: Video Compression with Hierarchical Encoding-based Neural Representationcited by 71
  2. 2024VEnhancer: Generative Space-Time Enhancement for Video Generationcited by 64
  3. 2022FFNeRV: Flow-Guided Frame-Wise Neural Representations for Videoscited by 46
  4. 2023DNeRV: Modeling Inherent Dynamics via Difference Neural Representation for Videoscited by 42
  5. 2023Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-Resolutioncited by 33
  6. 2022Scalable Neural Video Representations with Learnable Positional Featurescited by 29
  7. 2023CuNeRF: Cube-Based Neural Radiance Field for Zero-Shot Medical Image Arbitrary-Scale Super Resolutioncited by 28
  8. 2024Sparse Global Matching for Video Frame Interpolation with Large Motioncited by 28
  9. 2025EDEN: Enhanced Diffusion for High-quality Large-motion Video Frame Interpolationcited by 19
  10. 2023AnyFlow: Arbitrary Scale Optical Flow with Implicit Neural Representationcited by 17
  11. 2024HIIF: Hierarchical Encoding based Implicit Image Function for Continuous Super-resolutioncited by 17
  12. 2024DDMI: Domain-Agnostic Latent Diffusion Models for Synthesizing High-Quality Implicit Neural Representationscited by 16
38 more
  1. 2024Data-Efficient Unsupervised Interpolation Without Any Intermediate Frame for 4D Medical Imagescited by 16
  2. 2024Generalizable Implicit Motion Modeling for Video Frame Interpolationcited by 16
  3. 2026LUVE : Latent-Cascaded Ultra-High-Resolution Video Generation with Dual Frequency Expertscited by 15
  4. 2024Knowledge-guided Machine Learning: Current Trends and Future Prospectscited by 14
  5. 2024FreSh: Frequency Shifting for Accelerated Neural Representation Learningcited by 14
  6. 2023SportsSloMo: A New Benchmark and Baselines for Human-centric Video Frame Interpolationcited by 11
  7. 2024Fast Encoding and Decoding for Implicit Video Representationcited by 11
  8. 2023UniINR: Event-guided Unified Rolling Shutter Correction, Deblurring, and Interpolationcited by 9
  9. 2024Arbitrary-Scale Video Super-Resolution with Structural and Textural Priorscited by 9
  10. 2024VeGaS: Video Gaussian Splattingcited by 9
  11. 2025SNeRV: Spectra-preserving Neural Representation for Videocited by 9
  12. 2025BF-STVSR: B-Splines and Fourier-Best Friends for High Fidelity Spatial-Temporal Video Super-Resolutioncited by 9
  13. 2023Scale-Adaptive Feature Aggregation for Efficient Space-Time Video Super-Resolutioncited by 8
  14. 2024Imaging Interiors: An Implicit Solution to Electromagnetic Inverse Scattering Problemscited by 8
  15. 2025Optimizing Rank for High-Fidelity Implicit Neural Representationscited by 8
  16. 2022A unified model for continuous conditional video predictioncited by 7
  17. 2022INR-V: A Continuous Representation Space for Video-based Generative Taskscited by 7
  18. 2024Observation-Guided Meteorological Field Downscaling at Station Scale: A Benchmark and a New Methodcited by 7
  19. 2025MedFuncta: A Unified Framework for Learning Efficient Medical Neural Fieldscited by 7
  20. 2024HR-INR: Continuous Space-Time Video Super-Resolution via Event Cameracited by 6
  21. 2024Attention Beats Linear for Fast Implicit Neural Representation Generationcited by 6
  22. 2024Efficient Long Video Tokenization via Coordinate-based Patch Reconstructioncited by 6
  23. 2023FFEINR: Flow Feature-Enhanced Implicit Neural Representation for Spatio-temporal Super-Resolutioncited by 5
  24. 2024TSINR: Capturing Temporal Continuity via Implicit Neural Representations for Time Series Anomaly Detectioncited by 5
  25. 2025Arbitrary-Scale 3D Gaussian Super-Resolutioncited by 5
  26. 2023DeblurSR: Event-Based Motion Deblurring Under the Spiking Representationcited by 4
  27. 2023Coordinate-Aware Modulation for Neural Fieldscited by 4
  28. 2025Foundation Models for Environmental Science: A Survey of Emerging Frontierscited by 4
  29. 2025Continuous Representation Methods, Theories, and Applications: An Overview and Perspectivescited by 4
  30. 2026Towards Holistic Modeling for Video Frame Interpolation with Auto-regressive Diffusion Transformerscited by 4
  31. 2024Space-Time Video Super-resolution with Neural Operatorcited by 3
  32. 2024CPT-Interp: Continuous sPatial and Temporal Motion Modeling for 4D Medical Image Interpolationcited by 3
  33. 2025REGEN: Learning Compact Video Embedding with (Re-)Generative Decodercited by 3
  34. 2025StereoINR: Cross-View Geometry Consistent Stereo Super Resolution with Implicit Neural Representationcited by 3
  35. 2025Continuous Space-Time Video Super-Resolution with 3D Fourier Fieldscited by 3
  36. 2022Enhancing Space-time Video Super-resolution via Spatial-temporal Feature Interactioncited by 2
  37. 2023Tsanet: Temporal and Scale Alignment for Unsupervised Video Object Segmentationcited by 2
  38. 2023Positional Embedding-Aware Activationscited by 2

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