# Deformable 3D Convolution for Video Super-Resolution

arXiv: https://arxiv.org/abs/2004.02803 · HTML: https://mlsanity.com/paper/2004.02803/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2020.

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

- [Rethinking Alignment in Video Super-Resolution Transformers](https://mlsanity.com/paper/2207.08494/) (2022)
- [A Keypoint-based Global Association Network for Lane Detection](https://mlsanity.com/paper/2204.07335/) (2022)
- [Look Back and Forth: Video Super-Resolution with Explicit Temporal Difference Modeling](https://mlsanity.com/paper/2204.07114/) (2022)
- [Local-Global Temporal Difference Learning for Satellite Video Super-Resolution](https://mlsanity.com/paper/2304.04421/) (2023)
- [Multi-event Video-Text Retrieval](https://mlsanity.com/paper/2308.11551/) (2023)
- [Facial Depth and Normal Estimation using Single Dual-Pixel Camera](https://mlsanity.com/paper/2111.12928/) (2021)
- [Development of Skip Connection in Deep Neural Networks for Computer Vision and Medical Image Analysis: A Survey](https://mlsanity.com/paper/2405.01725/) (2024)
- [Large Motion Video Super-Resolution with Dual Subnet and Multi-Stage Communicated Upsampling](https://mlsanity.com/paper/2103.11744/) (2021)
- [Infrared Small Target Detection in Satellite Videos: A New Dataset and A Novel Recurrent Feature Refinement Framework](https://mlsanity.com/paper/2409.12448/) (2024)
- [Local Motion and Contrast Priors Driven Deep Network for Infrared Small Target Super-Resolution](https://mlsanity.com/paper/2201.01014/) (2022)
- [Progressive Training of A Two-Stage Framework for Video Restoration](https://mlsanity.com/paper/2204.09924/) (2022)
- [Towards High-Quality and Efficient Video Super-Resolution via Spatial-Temporal Data Overfitting](https://mlsanity.com/paper/2303.08331/) (2023)
- [NTIRE 2023 Challenge on Light Field Image Super-Resolution: Dataset, Methods and Results](https://mlsanity.com/paper/2304.10415/) (2023)
- [STDAN: Deformable Attention Network for Space-Time Video Super-Resolution](https://mlsanity.com/paper/2203.06841/) (2022)
- [Exploiting Optical Flow Guidance for Transformer-Based Video Inpainting](https://mlsanity.com/paper/2301.10048/) (2023)
- [NegVSR: Augmenting Negatives for Generalized Noise Modeling in Real-World Video Super-Resolution](https://mlsanity.com/paper/2305.14669/) (2023)
- [DeTurb: Atmospheric Turbulence Mitigation with Deformable 3D Convolutions and 3D Swin Transformers](https://mlsanity.com/paper/2407.20855/) (2024)
- [Towards True Detail Restoration for Super-Resolution: A Benchmark and a Quality Metric](https://mlsanity.com/paper/2203.08923/) (2022)
- [DeformUX-Net: Exploring a 3D Foundation Backbone for Medical Image Segmentation with Depthwise Deformable Convolution](https://mlsanity.com/paper/2310.00199/) (2023)
- [A Survey of Deep Learning Video Super-Resolution](https://mlsanity.com/paper/2506.03216/) (2025)
- [CVSformer: Cross-View Synthesis Transformer for Semantic Scene Completion](https://mlsanity.com/paper/2307.07938/) (2023)
- [Collaborative Feedback Discriminative Propagation for Video Super-Resolution](https://mlsanity.com/paper/2404.04745/) (2024)
- [Multi-frame Joint Enhancement for Early Interlaced Videos](https://mlsanity.com/paper/2109.14151/) (2021)
- [High Dynamic Range Imaging of Dynamic Scenes with Saturation Compensation but without Explicit Motion Compensation](https://mlsanity.com/paper/2308.11140/) (2023)
- [Tracking-Assisted Object Detection with Event Cameras](https://mlsanity.com/paper/2403.18330/) (2024)
- [Deep Learning Empowered Super-Resolution: A Comprehensive Survey and Future Prospects](https://mlsanity.com/paper/2509.22692/) (2025)
- [A Novel Dual Dense Connection Network for Video Super-resolution](https://mlsanity.com/paper/2203.02723/) (2022)
- [A Lightweight Recurrent Grouping Attention Network for Video Super-Resolution](https://mlsanity.com/paper/2309.13940/) (2023)
- [PTSR: Patch Translator for Image Super-Resolution](https://mlsanity.com/paper/2310.13216/) (2023)
- [Data Overfitting for On-Device Super-Resolution with Dynamic Algorithm and Compiler Co-Design](https://mlsanity.com/paper/2407.02813/) (2024)

## Abstract (arXiv)

The spatio-temporal information among video sequences is significant for video super-resolution (SR). However, the spatio-temporal information cannot be fully used by existing video SR methods since spatial feature extraction and temporal motion compensation are usually performed sequentially. In this paper, we propose a deformable 3D convolution network (D3Dnet) to incorporate spatio-temporal information from both spatial and temporal dimensions for video SR. Specifically, we introduce deformable 3D convolution (D3D) to integrate deformable convolution with 3D convolution, obtaining both superior spatio-temporal modeling capability and motion-aware modeling flexibility. Extensive experiments have demonstrated the effectiveness of D3D in exploiting spatio-temporal information. Comparative results show that our network achieves state-of-the-art SR performance. Code is available at: https://github.com/XinyiYing/D3Dnet.