# WAFT: Warping-Alone Field Transforms for Optical Flow

arXiv: https://arxiv.org/abs/2506.21526 · HTML: https://mlsanity.com/paper/2506.21526/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 49 of its 71 references that are in the map.
- First posted on arXiv 26 Jun 2025.

## Builds on (most cited first, 30 of 49)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021)
- [Depth Anything V2](https://mlsanity.com/paper/2406.09414/) (2024)
- [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)
- [A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation](https://mlsanity.com/paper/1512.02134/) (2015)
- [FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks](https://mlsanity.com/paper/1612.01925/) (2016)
- [TartanAir: A Dataset to Push the Limits of Visual SLAM](https://mlsanity.com/paper/2003.14338/) (2020)
- [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)
- [FlowFormer: A Transformer Architecture for Optical Flow](https://mlsanity.com/paper/2203.16194/) (2022)
- [Unifying Flow, Stereo and Depth Estimation](https://mlsanity.com/paper/2211.05783/) (2022)
- [Learning to Estimate Hidden Motions with Global Motion Aggregation](https://mlsanity.com/paper/2104.02409/) (2021)
- [Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art](https://mlsanity.com/paper/1704.05519/) (2017)
- [SEA-RAFT: Simple, Efficient, Accurate RAFT for Optical Flow](https://mlsanity.com/paper/2405.14793/) (2024)
- [Spring: A High-Resolution High-Detail Dataset and Benchmark for Scene Flow, Optical Flow and Stereo](https://mlsanity.com/paper/2303.01943/) (2023)
- [Playing for Benchmarks](https://mlsanity.com/paper/1709.07322/) (2017)
- [CroCo v2: Improved Cross-view Completion Pre-training for Stereo Matching and Optical Flow](https://mlsanity.com/paper/2211.10408/) (2022)
- [CroCo: Self-Supervised Pre-training for 3D Vision Tasks by Cross-View Completion](https://mlsanity.com/paper/2210.10716/) (2022)
- [Quadratic video interpolation](https://mlsanity.com/paper/1911.00627/) (2019)
- [MaskFlownet: Asymmetric Feature Matching with Learnable Occlusion Mask](https://mlsanity.com/paper/2003.10955/) (2020)
- [The Surprising Effectiveness of Diffusion Models for Optical Flow and Monocular Depth Estimation](https://mlsanity.com/paper/2306.01923/) (2023)
- [FlowFormer++: Masked Cost Volume Autoencoding for Pretraining Optical Flow Estimation](https://mlsanity.com/paper/2303.01237/) (2023)
- [Models Matter, So Does Training: An Empirical Study of CNNs for Optical Flow Estimation](https://mlsanity.com/paper/1809.05571/) (2018)

## Built on by (most cited first, 30 of 34)

- [CoWTracker: Tracking by Warping instead of Correlation](https://mlsanity.com/paper/2602.04877/) (2026)
- [MegaFlow: Zero-Shot Large Displacement Optical Flow](https://mlsanity.com/paper/2603.25739/) (2026)
- [WAFT-Stereo: Warping-Alone Field Transforms for Stereo Matching](https://mlsanity.com/paper/2603.24836/) (2026)
- [SimpleProc: Fully Procedural Synthetic Data from Simple Rules for Multi-View Stereo](https://mlsanity.com/paper/2604.04925/) (2026)
- [GeoFlow: Enforcing Implicit Geometric Consistency in Video Generation](https://mlsanity.com/paper/2605.18365/) (2026)
- [DynTrace: Tracking Dynamic Object Evidence for 4D Spatio-Temporal Reasoning in MLLMs](https://mlsanity.com/paper/2607.12503/) (2026)
- [4DGS-WAM: Bridging Past and Future with an Object-Centric World Action Model based on 4D Gaussian Splatting](https://mlsanity.com/paper/2608.25956/) (2026)
- [Towards Reliable Sea Ice Drift Estimation in the Arctic Deep Learning Optical Flow on RADARSAT-2](https://mlsanity.com/paper/2510.26653/) (2025)
- [SurfPhase: 3D Interfacial Dynamics in Two-Phase Flows from Sparse Videos](https://mlsanity.com/paper/2602.11154/) (2026)
- [PoseFM: Relative Camera Pose Estimation Through Flow Matching](https://mlsanity.com/paper/2604.22350/) (2026)
- [TrAction: Action Recognition with Sparse Trajectories](https://mlsanity.com/paper/2606.03490/) (2026)
- [ViSTR-Bench: Can MLLMs Reason from Continuous Visual Cues in Dynamic Scenes?](https://mlsanity.com/paper/2607.20868/) (2026)
- [MachEmbodied-U0: Unified Understanding and Generation Model for Embodied Intelligence](https://mlsanity.com/paper/2609.25627/) (2026)
- [Match4Annotate: Cross-Video Annotation Transfer in Ultrasound via Implicit Feature Flow-Guided Matching](https://mlsanity.com/paper/2603.06471/) (2026)
- [RetimeGS: Continuous-Time Reconstruction of 4D Gaussian Splatting](https://mlsanity.com/paper/2603.13783/) (2026)
- [FlowIt: Global Matching via Hierarchical Transformers and Optimal Transport for Optical Flow](https://mlsanity.com/paper/2603.28759/) (2026)
- [DynaVid: Learning to Generate Highly Dynamic Videos using Synthetic Motion Data](https://mlsanity.com/paper/2604.01666/) (2026)
- [Flow of Truth: Proactive Temporal Forensics for Image-to-Video Generation](https://mlsanity.com/paper/2604.15003/) (2026)
- [JacobianAvatar: Temporally Consistent Semi-rigid Avatar Reconstruction from a Monocular Video](https://mlsanity.com/paper/2606.31115/) (2026)
- [On the Real-World Generalisability of Optical Flow Models](https://mlsanity.com/paper/2607.10470/) (2026)
- [Point Tracking in Surgery--The 2025 Surgical Tattoos in Infrared Challenge (STIRC2025)](https://mlsanity.com/paper/2607.12939/) (2026)
- [WAVE-Stereo: Warp-Aligned Volume Encoding for Stereo Matching](https://mlsanity.com/paper/2607.13674/) (2026)
- [Optical Flow from Photons](https://mlsanity.com/paper/2608.00499/) (2026)
- [Surg-UniWorld: A Unified Surgical World Model with Multimodal Control Experts](https://mlsanity.com/paper/2608.06770/) (2026)
- [WiFlow: Estimating Optical Flow using WiFi Channel State Information](https://mlsanity.com/paper/2609.02452/) (2026)
- [FreeFlow: A Bias-free Hierarchical Transformer for Optical Flow Estimation](https://mlsanity.com/paper/2609.11486/) (2026)
- [TopoRig: Topology-Agnostic Facial Rigging via Multi-Source Supervision](https://mlsanity.com/paper/2609.15746/) (2026)
- [Bi-FlowGS: Bridging Generative View Completion and Gaussian Geometry through Bidirectional Flow Co-Refinement](https://mlsanity.com/paper/2609.17039/) (2026)
- [TrackEverything: Long Horizon Dense Tracking via De-Duplicating 3D Scene Representations](https://mlsanity.com/paper/2609.30222/) (2026)
- [Unifying Video Tasks via Spatiotemporal Analogy](https://mlsanity.com/paper/2609.33935/) (2026)

## Abstract (arXiv)

We introduce Warping-Alone Field Transforms (WAFT), a simple and effective method for optical flow. WAFT is similar to RAFT but replaces cost volume with high-resolution warping, achieving better accuracy with lower memory cost. This design challenges the conventional wisdom that constructing cost volumes is necessary for strong performance. WAFT is a simple and flexible meta-architecture with minimal inductive biases and reliance on custom designs. Compared with existing methods, WAFT ranks 1st on Spring, Sintel, and KITTI benchmarks, achieves the best zero-shot generalization on KITTI, while being 1.3-4.1x faster than existing methods that have competitive accuracy (e.g., 1.3x than Flowformer++, 4.1x than CCMR+). Code and model weights are available at hrefhttps://github.com/princeton-vl/WAFThttps://github.com/princeton-vl/WAFT.