# ST-MFNet: A Spatio-Temporal Multi-Flow Network for Frame Interpolation

arXiv: https://arxiv.org/abs/2111.15483 · HTML: https://mlsanity.com/paper/2111.15483/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 43 of its 68 references that are in the map.
- First posted on arXiv 30 Nov 2021.

## Builds on (most cited first, 30 of 43)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Deformable Convolutional Networks](https://mlsanity.com/paper/1703.06211/) (2017)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume](https://mlsanity.com/paper/1709.02371/) (2017)
- [Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring](https://mlsanity.com/paper/1612.02177/) (2016)
- [Video Enhancement with Task-Oriented Flow](https://mlsanity.com/paper/1711.09078/) (2017)
- [The Perception-Distortion Tradeoff](https://mlsanity.com/paper/1711.06077/) (2017)
- [Quantifying the Carbon Emissions of Machine Learning](https://mlsanity.com/paper/1910.09700/) (2019)
- [Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation](https://mlsanity.com/paper/1712.00080/) (2017)
- [Softmax Splatting for Video Frame Interpolation](https://mlsanity.com/paper/2003.05534/) (2020)
- [Depth-Aware Video Frame Interpolation](https://mlsanity.com/paper/1904.00830/) (2019)
- [Video Frame Interpolation via Adaptive Separable Convolution](https://mlsanity.com/paper/1708.01692/) (2017)
- [Video Frame Synthesis using Deep Voxel Flow](https://mlsanity.com/paper/1702.02463/) (2017)
- [Temporal Generative Adversarial Nets with Singular Value Clipping](https://mlsanity.com/paper/1611.06624/) (2016)
- [DeepStereo: Learning to Predict New Views from the World's Imagery](https://mlsanity.com/paper/1506.06825/) (2015)
- [Video Frame Interpolation via Adaptive Convolution](https://mlsanity.com/paper/1703.07514/) (2017)
- [Optimizing the Latent Space of Generative Networks](https://mlsanity.com/paper/1707.05776/) (2017)
- [Context-aware Synthesis for Video Frame Interpolation](https://mlsanity.com/paper/1803.10967/) (2018)
- [Quadratic video interpolation](https://mlsanity.com/paper/1911.00627/) (2019)
- [Video Compression through Image Interpolation](https://mlsanity.com/paper/1804.06919/) (2018)
- [AdaCoF: Adaptive Collaboration of Flows for Video Frame Interpolation](https://mlsanity.com/paper/1907.10244/) (2019)
- [XVFI: eXtreme Video Frame Interpolation](https://mlsanity.com/paper/2103.16206/) (2021)
- [BMBC:Bilateral Motion Estimation with Bilateral Cost Volume for Video Interpolation](https://mlsanity.com/paper/2007.12622/) (2020)
- [MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and Enhancement](https://mlsanity.com/paper/1810.08768/) (2018)
- [Asymmetric Bilateral Motion Estimation for Video Frame Interpolation](https://mlsanity.com/paper/2108.06815/) (2021)
- [FLAVR: Flow-Agnostic Video Representations for Fast Frame Interpolation](https://mlsanity.com/paper/2012.08512/) (2020)

## Built on by (most cited first, 30 of 35)

- [Extracting Motion and Appearance via Inter-Frame Attention for Efficient Video Frame Interpolation](https://mlsanity.com/paper/2303.00440/) (2023)
- [LDMVFI: Video Frame Interpolation with Latent Diffusion Models](https://mlsanity.com/paper/2303.09508/) (2023)
- [Video Interpolation with Diffusion Models](https://mlsanity.com/paper/2404.01203/) (2024)
- [FloLPIPS: A Bespoke Video Quality Metric for Frame Interpoation](https://mlsanity.com/paper/2207.08119/) (2022)
- [TANGO: Co-Speech Gesture Video Reenactment with Hierarchical Audio Motion Embedding and Diffusion Interpolation](https://mlsanity.com/paper/2410.04221/) (2024)
- [Event-Based Video Frame Interpolation With Cross-Modal Asymmetric Bidirectional Motion Fields](https://mlsanity.com/paper/2502.13716/) (2025)
- [Framer: Interactive Frame Interpolation](https://mlsanity.com/paper/2410.18978/) (2024)
- [Perception-Oriented Video Frame Interpolation via Asymmetric Blending](https://mlsanity.com/paper/2404.06692/) (2024)
- [EDEN: Enhanced Diffusion for High-quality Large-motion Video Frame Interpolation](https://mlsanity.com/paper/2503.15831/) (2025)
- [A Perceptual Quality Metric for Video Frame Interpolation](https://mlsanity.com/paper/2210.01879/) (2022)
- [Motion-aware Latent Diffusion Models for Video Frame Interpolation](https://mlsanity.com/paper/2404.13534/) (2024)
- [Cross-Attention Transformer for Video Interpolation](https://mlsanity.com/paper/2207.04132/) (2022)
- [AceVFI: A Comprehensive Survey of Advances in Video Frame Interpolation](https://mlsanity.com/paper/2506.01061/) (2025)
- [ST-MFNet Mini: Knowledge Distillation-Driven Frame Interpolation](https://mlsanity.com/paper/2302.08455/) (2023)
- [Video Frame Interpolation with Densely Queried Bilateral Correlation](https://mlsanity.com/paper/2304.13596/) (2023)
- [JNMR: Joint Non-linear Motion Regression for Video Frame Interpolation](https://mlsanity.com/paper/2206.04231/) (2022)
- [BVI-VFI: A Video Quality Database for Video Frame Interpolation](https://mlsanity.com/paper/2210.00823/) (2022)
- [Sparsity-guided Network Design for Frame Interpolation](https://mlsanity.com/paper/2209.04551/) (2022)
- [A Deep Learning Approach to Generating Photospheric Vector Magnetograms of Solar Active Regions for SOHO/MDI Using SDO/HMI and BBSO Data](https://mlsanity.com/paper/2211.02278/) (2022)
- [ZeroSmooth: Training-free Diffuser Adaptation for High Frame Rate Video Generation](https://mlsanity.com/paper/2406.00908/) (2024)
- [Video Motion Graphs](https://mlsanity.com/paper/2503.20218/) (2025)
- [Flow Guidance Deformable Compensation Network for Video Frame Interpolation](https://mlsanity.com/paper/2211.12117/) (2022)
- [MoSt-DSA: Modeling Motion and Structural Interactions for Direct Multi-Frame Interpolation in DSA Images](https://mlsanity.com/paper/2407.07078/) (2024)
- [Video Frame Interpolation with Many-to-many Splatting and Spatial Selective Refinement](https://mlsanity.com/paper/2310.18946/) (2023)
- [EventDiff: A Unified and Efficient Diffusion Model Framework for Event-based Video Frame Interpolation](https://mlsanity.com/paper/2505.08235/) (2025)
- [AdaFNIO: Adaptive Fourier Neural Interpolation Operator for video frame interpolation](https://mlsanity.com/paper/2211.10791/) (2022)
- [Survey on Visual Signal Coding and Processing with Generative Models: Technologies, Standards and Optimization](https://mlsanity.com/paper/2405.14221/) (2024)
- [SPEED: One-Step Pixel Diffusion for High-quality Video Frame Interpolation](https://mlsanity.com/paper/2607.15585/) (2026)
- [DaBiT: Depth and Blur informed Transformer for Video Focal Deblurring](https://mlsanity.com/paper/2407.01230/) (2024)
- [Adapting Image-to-Video Diffusion Models for Large-Motion Frame Interpolation](https://mlsanity.com/paper/2412.17042/) (2024)

## Abstract (arXiv)

Video frame interpolation (VFI) is currently a very active research topic, with applications spanning computer vision, post production and video encoding. VFI can be extremely challenging, particularly in sequences containing large motions, occlusions or dynamic textures, where existing approaches fail to offer perceptually robust interpolation performance. In this context, we present a novel deep learning based VFI method, ST-MFNet, based on a Spatio-Temporal Multi-Flow architecture. ST-MFNet employs a new multi-scale multi-flow predictor to estimate many-to-one intermediate flows, which are combined with conventional one-to-one optical flows to capture both large and complex motions. In order to enhance interpolation performance for various textures, a 3D CNN is also employed to model the content dynamics over an extended temporal window. Moreover, ST-MFNet has been trained within an ST-GAN framework, which was originally developed for texture synthesis, with the aim of further improving perceptual interpolation quality. Our approach has been comprehensively evaluated -- compared with fourteen state-of-the-art VFI algorithms -- clearly demonstrating that ST-MFNet consistently outperforms these benchmarks on varied and representative test datasets, with significant gains up to 1.09dB in PSNR for cases including large motions and dynamic textures. Project page: https://danielism97.github.io/ST-MFNet.