# BiFormer: Learning Bilateral Motion Estimation via Bilateral Transformer for 4K Video Frame Interpolation

arXiv: https://arxiv.org/abs/2304.02225 · HTML: https://mlsanity.com/paper/2304.02225/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 44 of its 54 references that are in the map.
- First posted on arXiv 5 Apr 2023.

## Builds on (most cited first, 30 of 44)

- [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)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [Swin Transformer V2: Scaling Up Capacity and Resolution](https://mlsanity.com/paper/2111.09883/) (2021)
- [FlowNet: Learning Optical Flow with Convolutional Networks](https://mlsanity.com/paper/1504.06852/) (2015)
- [PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume](https://mlsanity.com/paper/1709.02371/) (2017)
- [FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks](https://mlsanity.com/paper/1612.01925/) (2016)
- [Video Enhancement with Task-Oriented Flow](https://mlsanity.com/paper/1711.09078/) (2017)
- [Twins: Revisiting the Design of Spatial Attention in Vision Transformers](https://mlsanity.com/paper/2104.13840/) (2021)
- [Stand-Alone Self-Attention in Vision Models](https://mlsanity.com/paper/1906.05909/) (2019)
- [Optical Flow Estimation using a Spatial Pyramid Network](https://mlsanity.com/paper/1611.00850/) (2016)
- [GMFlow: Learning Optical Flow via Global Matching](https://mlsanity.com/paper/2111.13680/) (2021)
- [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)
- [FlowFormer: A Transformer Architecture for Optical Flow](https://mlsanity.com/paper/2203.16194/) (2022)
- [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)
- [Real-Time Intermediate Flow Estimation for Video Frame Interpolation](https://mlsanity.com/paper/2011.06294/) (2020)
- [Video Frame Synthesis using Deep Voxel Flow](https://mlsanity.com/paper/1702.02463/) (2017)
- [UnFlow: Unsupervised Learning of Optical Flow with a Bidirectional Census Loss](https://mlsanity.com/paper/1711.07837/) (2017)
- [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)
- [Context-aware Synthesis for Video Frame Interpolation](https://mlsanity.com/paper/1803.10967/) (2018)
- [FILM: Frame Interpolation for Large Motion](https://mlsanity.com/paper/2202.04901/) (2022)
- [DF-Net: Unsupervised Joint Learning of Depth and Flow using Cross-Task Consistency](https://mlsanity.com/paper/1809.01649/) (2018)
- [Learning-Based View Synthesis for Light Field Cameras](https://mlsanity.com/paper/1609.02974/) (2016)
- [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)

## Built on by (most cited first, 23 of 23)

- [TANGO: Co-Speech Gesture Video Reenactment with Hierarchical Audio Motion Embedding and Diffusion Interpolation](https://mlsanity.com/paper/2410.04221/) (2024)
- [Sparse Global Matching for Video Frame Interpolation with Large Motion](https://mlsanity.com/paper/2404.06913/) (2024)
- [EDEN: Enhanced Diffusion for High-quality Large-motion Video Frame Interpolation](https://mlsanity.com/paper/2503.15831/) (2025)
- [PMQ-VE: Progressive Multi-Frame Quantization for Video Enhancement](https://mlsanity.com/paper/2505.12266/) (2025)
- [Data-Efficient Unsupervised Interpolation Without Any Intermediate Frame for 4D Medical Images](https://mlsanity.com/paper/2404.01464/) (2024)
- [BiM-VFI: Bidirectional Motion Field-Guided Frame Interpolation for Video with Non-uniform Motions](https://mlsanity.com/paper/2412.11365/) (2024)
- [Motion-Aware Generative Frame Interpolation](https://mlsanity.com/paper/2501.03699/) (2025)
- [Disentangled Motion Modeling for Video Frame Interpolation](https://mlsanity.com/paper/2406.17256/) (2024)
- [AceVFI: A Comprehensive Survey of Advances in Video Frame Interpolation](https://mlsanity.com/paper/2506.01061/) (2025)
- [A Survey on Future Frame Synthesis: Bridging Deterministic and Generative Approaches](https://mlsanity.com/paper/2401.14718/) (2024)
- [TLB-VFI: Temporal-Aware Latent Brownian Bridge Diffusion for Video Frame Interpolation](https://mlsanity.com/paper/2507.04984/) (2025)
- [FB-Diff: Fourier Basis-guided Diffusion for Temporal Interpolation of 4D Medical Imaging](https://mlsanity.com/paper/2507.04547/) (2025)
- [Benchmarking Video Frame Interpolation](https://mlsanity.com/paper/2403.17128/) (2024)
- [Dual-Camera Smooth Zoom on Mobile Phones](https://mlsanity.com/paper/2404.04908/) (2024)
- [CPT-Interp: Continuous sPatial and Temporal Motion Modeling for 4D Medical Image Interpolation](https://mlsanity.com/paper/2405.15385/) (2024)
- [One Step Closer: Creating the Future to Boost Monocular Semantic Scene Completion](https://mlsanity.com/paper/2507.13801/) (2025)
- [IDO-VFI: Identifying Dynamics via Optical Flow Guidance for Video Frame Interpolation with Events](https://mlsanity.com/paper/2305.10198/) (2023)
- [Velocity Disambiguation for Video Frame Interpolation](https://mlsanity.com/paper/2311.08007/) (2023)
- [Restoration-Oriented Video Frame Interpolation with Region-Distinguishable Priors from SAM](https://mlsanity.com/paper/2312.15868/) (2023)
- [Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain](https://mlsanity.com/paper/2411.16123/) (2024)
- [LiveStre4m: Feed-Forward Live Streaming of Novel Views from Unposed Multi-View Video](https://mlsanity.com/paper/2604.06740/) (2026)
- [MASS: Motion-Aligned Selective Scan for Refinement in Flow-Based Video Frame Interpolation](https://mlsanity.com/paper/2606.27718/) (2026)
- [4DHumanDiff: Direct Text-to-4DGS Generation for Consistent 360-Degree Dynamic Humans](https://mlsanity.com/paper/2607.27634/) (2026)

## Abstract (arXiv)

A novel 4K video frame interpolator based on bilateral transformer (BiFormer) is proposed in this paper, which performs three steps: global motion estimation, local motion refinement, and frame synthesis. First, in global motion estimation, we predict symmetric bilateral motion fields at a coarse scale. To this end, we propose BiFormer, the first transformer-based bilateral motion estimator. Second, we refine the global motion fields efficiently using blockwise bilateral cost volumes (BBCVs). Third, we warp the input frames using the refined motion fields and blend them to synthesize an intermediate frame. Extensive experiments demonstrate that the proposed BiFormer algorithm achieves excellent interpolation performance on 4K datasets. The source codes are available at https://github.com/JunHeum/BiFormer.