WAFT: Warping-Alone Field Transforms for Optical Flow
Authors: Yihan Wang
Jia Deng
Venue: ICLR 2026 Oral (2026)
Keywords: Optical Flow; Computer Vision; Warping; Dense Correspondences
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: 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 https://github.com/princeton-vl/WAFT.

Published as a conference paper at ICLR 2026
WAFT: WARPING-ALONE FIELD TRANSFORMS FOR OPTICAL FLOW
Yihan Wang & Jia Deng
Department of Computer Science Princeton University (yw7685, jiadeng
ABSTRACT
We introduce Warping-Alone Field Transforms (WAFT), 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 nec- essary for strong performance. WAFT is a simple and flexible meta- architeccie 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 (c.g., 1.3x than Flowformer++, 4.1x than CCMR+). Code and model weights are available at http://ghhhbbbcomm/iiiceooo-W//
INTRODUCTION
Optical flow is a fundamental low-level vision task that estimates per-pixel 2D motion between
video frames. It has many downstream applications, including 3D reconstruction and synthesis (Ma
ct al.
2022 Zuo & Deng 2022), action recognition (Sun et al. 20188) Piergiovanni Ryoo 2019
Zhao et al. 2020b)) frame interpolation (Xu et al., 2019 Liu al. 22000] Huang al.) 2020). and
autonomous driving (Geiger et al. 2013 Menze & Geiger 2015 Janai et al. (0220).
2017) with iterative updates (Teed & Deng 2020 Wang ct al. 2024) has become a standard design in most state-of-the-art methods (Sun et al.] 8a Dosovitskiy al. 2015 Xu et al. 2017 Teed & Deng 2020 Huang al., 2022 Wang et al. 2024 Morimiius et al., (2225), especially when both accuracy and efficiency are taken into account. Previous work (Sun et al. [0188| Teed & Deng 2020) regards cost volumes more effective representation than image features, as it explicitly models the visual similarity between pixels.
Cost volumes (Sun et al. 2018 1gg et al.
However, constructing cost volumes expensive in both time and memory (Zhao al. 2024 Xu et al. 2023a). The cost increases quadratically with the radius of the neighborhood. As result, cost volumes are often constructed from low resolution features, limiting the ability of the model to handle high-resolution input images.
In this paper, we challenge the conventional wisdom that cost volume is necessary for strong perfor- mance with high efficiency, and introduce Warping-Alone Field Transforms (WAFT), simplified design that replaces cost volumes with warping and achieves state-of-the-ar accuracy across various benchmarks with high efficiency.
For each pixel frame 1, instead of computing its similarities against many pixels frame 2, warp- ing simply fetches the feature vector of the corresponding pixel given by the current flow estimate; this enables memory-efficiae high-resolution processing and leads to better accuracy.
The design of WAFT is simple, with flow-specific designs kept to the minimum. WAFT consists of an input encoder that extracts features from individual input frames and a recurrent update module that iteratively updates flow. Compared to other RAFT-like architectures, WAFT is much simplified because does not use cost volumes and has removed the context encoder that provides extra fea- tures for the update module. WAFT is designed to function meta-architecture for optical flow in

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Published as a conference paper at ICLR 2026
X
?
Image
f
Recurrent Update
Input Encoder
Module
h+1
Feature 2
Feature 1
Feature
Feature Extraction
Feature-Space Warping and Iterative Updates
The meta-archilecttre WAFT consists of an input encoder recurrent update module. first extract image features from the input encoder, and then use these features to iteratively update the flow estimate for T steps. At each step, perform feature indexing through a lightweight backward warping on the feature of frame 2, removing the dependency expensive cost volume used previous work.
Figure I:
the sense that the individual components including input encoder and the update unit do not require custom designs and can use existing off-the-shelf (pretrained) architectures. In our experiments, evaluate different choices (such as ResNet (He et al. 2016 and DPT (Ranful et al. 202Ih) that have different eecuracy-efficiinng trade-offs.
WAFT achieves state-of-the-arr performance across various benchmarks with high efficiency and a simple design. Using Twins (Chu et al. 2021) backbone pre-trained only on ImageNet, WAFT ranks first on Spring, second on KITTI, and is competitive on Sintel. It also achieves the best zero-shot cross-dataset generalization on KITTI. Using a stronger backbone, depth depth-pretrained DAv2 Yangg al., (024)), WAFT outperforms existing methods on all public benchmarks, We achieve this with standard network architectures for the sub-modules (Dosovitskiy et al. [0200] He al. 2016 Ranftl et al. 2021), removing custom designs typically needed in prior work, while 1.3
being
1.1x faster than existing methods that have competitive accuracy (e.g., 1.3x than Flow-
former++ Shi et al. (2023), 4.1x than CCMR+ Jahedi et al. (2024)).
JJaedi
Our main contributions are two-fold: (1) we challenge the conventional wisdom that cost volume key component for achieving state-of-the-ar accuracy and efficiency for optical flow; (2) introduce WAFT, a warping-based meta-architecture that is simpler and achieves state-of-the-art accuracy with high efficiency.
RELATED WORK
Estimating Optical Flow Traditional methods treated optical flow as global optimization prob- lem that maximizes visual similarity between corresponding pixels (Horn Schunck, 198T Zach
et al. 2007 Chen & Koltun 2016; Brox et al.] 2004). These methods apply coarse-to to-fee- warp-
ing Brox et al. [2004] Black & Anandam 1996 Memin & Perez 1998), a strategy theoretically justified by Brox et al. (Brox al. 20044), solve this optimization.
Today, this field is dominated by deep learning methods (Ilg et al., 2017 Dosovitskiy et al. 2015 2020a; Hui al.)
Sun al.) [018a] Zhao al.
2018 Teed & Deng 2020 Sui et al. 2022
Sun
et al. [2022] [Deg et al.
2023 Huang et al.]
2022]60
Shi et al. [2033 Weinzaepfel et al.) 2022
2023
Xu al. [2022] 2023b
Leroy al. 2023
Saxena al. 2024 Jahedi al. 2024
2023
Luo
et al. 2heng et al.
2022 Zhao et al.
2022 Luo el al. 2023 Jung et al. 2023 Luo el al.
2024
Zhou et al. 2024,
Morimitsu et al. 2025). J which can categorized into paradigms:
direct or iterative. Direct methods (Dosovitskiy et al. 2015 /Weinaepfel et al.) 2022, 2023 Saxena
al. 2024 Leroy et al. 2023] Xu et al. (022)) treat flow estimation as a standard dense prediction task (e.g. monocular depth estimation) and directly regress the dense flow field from large-scale
pre-trained models. Iterative methods (Teed & Deng 2020 Wang et al. 2024 Luo et al. 2024]
Morimitsu et al. 2025 Zhou et al. 2024 Sun et al. 201 8a Huang et al. 2022 align more closely with traditional warping-basse based approaches. refining the flow predictions progressively. Most state-
of-the-art methods (Teed & Deng 2020 Wang et al. 2024 Huang al. 2022 Shi et al. 2023
Luo et al. 2024| Morimitsu et al. 2025) follow the iterative paradigm due to its significantly higher
efficiency than the direct ones.
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Full Cost Volume
Partial Cost Volume
Warping
Figure 2: For each pixel, the full cost volume calculates its visual similarity all pixels in the other frame through correlation. The partial cost volume restricts the search range the neighborhood of the corresponding pixel, marked by red box. Compared with them, warping only uses the information from the corresponding pixel, offering better time and memory efficiency. This efficiency enables high-resolution processing, which leads to improved accuracy.
Cost volumes (Sun et al. 2018a Dosovitskiy et al. 2015) have been regarded as a standard design in iterative methods ("Tee & Deng 2020 Wang al. [2024] Morimitsu al.] [025 Huang al.
2022
Shi et al. 2023 Xu et al.) 2023b Luo et al. [024]. Prior work (Teed & Deng 2020] Suun
al.
201 empirically shows the effectiveness of cost volumes in handling large displacements.
Many iterative methods (Teed & Deng 2020 Wang et al.) 2224]] Morimitsu et al. 2025) adopt
partial cost volumes (Sun et al., 2018a) avoid the quadratic computational complexity of full 4D cost volumes; they restrict the search range of each pixel in frame I to the neighborhood of its corresponding pixel in frame
However, they still suffer from the high memory consumption
inherent in cost volumes (Xu et al. 2023a Zhao et al. 2024).
WAFT is warping-based iterative method. We achieve sfute-of-th--aee performance across various benchmarks without constructing cost volumes, challenging the conventional wisdom established by previous work (Sun et al., [208a] Teed Deng 22200, Huang et al.) (202).. Warping no longer suffers from high memory consumption inherent in cost volumes, which enables high-resolution indexing and therefore improves accuracy.
2020) have achieved significant progress across a wide range of visual tasks (Yang et al. 2024; Kirillov et al.) 2023 Oquab et al., 2023 He et al.) 2022) Rombach et al.] (002). In the context of optical flow, most direct meth- ods WWenzzaeffll et al.
Vision Transformers Vision transformers (Dosovitskiy
2022; 2023; Saxena et al., 2024) regress flow from a large-scale pre-trained vision transformer with lightweight flow head. Many iterative methods (Huang et al. 2022 Shi
et al. 2023, Luo et al. 2024 Zhou et al. 2024) design task-specific transformer blocks to process
cost volumes.
WAFT adopts similar designs to DPT (Ranfil et al. 202 in its recurrent update module, which implicitly handles large displacements in optical flow through the transformer architecture. We empirically show that this is crucial to make warping work. WAFT can also benefit from large-scale pre-trained transformers like existing methods (Saxena et al. 20024] Weinzaepfel et al.) 2022 2023 Zhou et al. 2024), with minimal additional flow-specific designs.
BACKGROUND
In this section, we first review current cost-volume-based iterative methods and discuss the draw- backs of cost volumes. Then we introduce warping and compare it to cost volumes.
3.1 ITERATIVE METHODS WITH COST VOLUME
Given two adjacent RGB frames, optical flow predicts pixel-wise 2D motion between adjacent
frames. Current iterative methods (Teed & Deng 2020 Huang al. 2022 Wang al.] 2024)

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Flow
DPFlow
SEA-RAFT
WAFT (Ours)
Figure 3: Visualizations of different methods on Spring (Mehl al 2023 WAFT, benefiting from high-
resolution indexing, obtains sharper boundaries and lower than low-resolution approaches.
consist of two parts: (1) input encoders that extract dense image features at low resolution, and (2) recurrent update module that iteratively refines the flow estimate.
Denoting the two frames as J1 I2 Hx the input encoder F maps J1, 12 low-resolution dense features F( F(T2) TRxxuxd respectively. A cost volume V E <Rxxxxxxx is built on these features, which explicitly models the correlation between pixels (p 1.,p' E 12):
VA = F(4)p (F(1)))F(12))
where represents the dot product of two vectors, At each step, the recurrent update module indexes into the cost volume using the current flow estimate and predicts residual flow update. Several methods (Huang et al. 2022 Shi et al. 2023) directly process V for better performance, but tend to be more costly.
Partial cost volume (Sun et al. 201 8a) is introduced reduce the cost by avoiding the construction of full 4D cost volume. Given the current flow estimate feur igxxxx2 and pre-defined look-up radius r. partial cost volume Vpar Rhxwxx + [hxww((")) implements an on-the-fly partial con- struction by restricting the indexing range of a pixel E I1 the neighborhood of its corresponding pixel + (feur)p b E I2, formulated as:
Vpar (feurir)
(Vo,, 12, s.t./pp (four)p -p'lla S r(l))
where the operator "coneat" concatenates all values inside the set into a vector. In practice, partial cost volumes are usually constructed at multiple scales (Teed & Deng 2020 Wang et al. 2024 to improve the prediction large displacements. Current methods also introduce context encoder (Sun
et al. 2018a Teed & Deng 2020 Huang et al. of iterative refinement.
2022 Wang et al. 2024) to enhance the effectiveness
3.2 DRAWBACKS OF COST VOLUMES
High Memory Cost The main drawback of cost volume its high memory consumption. Full partial cost volume high resolution very expensive and often infeasible (Zhao et al.] Xu et al. 2023a). Therefore, most itera- tive methods build the cost volume and index into it at 1/8 resolution. To further demonstrate the problem, we implement several variants of SEA-RAFT (Wang al. 2024) that build par- tial cost volumes at different resolutions. We set the base channel dimension as 32, 64, and 128 for the 1/2, 1/4, and 1/8 resolution variants, respectively, to make their computational cost simi- lar (around 350GMACs). As shown Table the partial cost volume with look-up radius in SEA-RAFT runs out of memory at 1/2 resolution. WAFT removes the reliance cost volume, and therefore consumes significantly lower memory than existing methods (Wang et al. 2024 Huang
Training Memory Cost (GiB)
Method
1/8 Reso.
1/4 Reso.
1/2 Reso.
SEA-RAFT
14.1
25.8
0OM
2024
Flowformer
26.1
CCMR+
36.0
WAFT-Twins-a2
7.6
9.2
Table profile the training memory cost with batch size on an RTX A6000. Our warping method signifi- cantly reduces the cost.
al.
2022
Jahedi al. 2024).
Error from Low Resolution Indexing Since cost volumes are restricted to low resolution, the predicted flow field must be downsampled for cost volume look-up, which inevitably introduces
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Frame1
Flow
Figure 4: Visualizations on Spring, KITTI, and Sintel public benchmarks (from left to right).
emoos. As illustrated in Figure uuiing an example from the Spring benchmark (Mehl et al. 2023), existing methods struggle to produce clear boundaries, particularly noticeable in the top-right corner. In contrast, our method benefits from high-resolution look-up feature vectors and obtains sharper boundary predictions in these challenging regions. We also show the quantitative performance gain from high-resolution indexing in Table
3.3 WARPING VS. COST VOLUME
Warping was widely used in both classical and early deep learning approaches (Memin & Perez 1998 Brox al. 2004 Olg al. 2017 Ranjan & Black 2017). However, most recent iterative methods WWang et al.) [2024] Teed & Deng 2020: Huang et al. (2022) have either replaced warping with cost volumes or used both in combination, since cost volumes have been shown remarkably improve the performance (Sun et al., 2018a). In this section, we analyze the similarities and dif- ferences between warping and cost volume (see Figure [ and argue that with appropriate designs, warping-based methods can achieve performance on par with cost-volume-basee methods.
The overlap of warping and cost volumes lies in their use of the current flow prediction to index into feature maps, which is closely related to optimization (Brox et al. 20004). In cost-volume-based iterative methods, the current flow estimate Seur used to define partial cost volume Vyar (see Section 3.1. It is also used define the warped feature map We Warp(Seur) E Rxxwxx, where the feature vector of pixel is indexed from the feature map of frame 2, formulated as:
Warp(feur), (11)))+(fa))
Compared to cost volumes, for each pixel in frame 1, warping does not calculate its visual similarity to multiple pixels in frame 2, making it no longer able to explicitly model large displacements. However, implicitly handle this long-range dependence through the attention mechanism in vision transformers (Dosovitskiy et al., 2020: Ranftl et al.) 2021), which, as we will demonstrate, is crucial make warping work well (see Section 5.5) The high memory efficiency of warping also enables high-resolution indexing, leading to improved accuracy.
4
METHOD
In this section, we describe WAFT, our varping-based iterative method, shown in Figure Its design can be understood as a simple meta-architecture that integrates an input encoder and a re- current update module. We will also discuss the advantages of our design, especially its strong performance and the simplifications over past designs.
Input Encoder We develop two ways to adapt large-scale seale pre-trained models. Adaptation (al) initial design specific to DAv2. We freeze the entire DAv2, incorporate features from its DPT head, and further refine these features using a ResNet18. Adaptation 2 (a2) is our improved design that works better and supports more backbones, where we only freeze the ViT/CNN backbones. We
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make the DPT head trainable and side-tune the features with 3 ResNet blocks. For completeness, we report the results of both.
Similar to existing iterative methods (Teed & Deng, 2020 Wang et al, 2024 Huang et al.] 2022). our recurrent update module R iteratively predicts the residual flow updates. At step t. concatenate F(I1) (feature of frame 1), (arr( (warped feature of frame 2, see Section 3.3 and the current hidden state Hidden, E Rax x as input. We use a slightly
Recurrent U Update Module
modified DPT RRafftl al. 2021) as the architecture of the module.
Prediction Head & Loss We adopt the Mixture-of-Lapieee (MoL) loss used in SEA-RAFT (Wang
al.
2024).
At step the hidden state Hidden, is used to predict the MoL. parameters M They are upsampled to the original image resolution through convex upsampling (Teed &
I R
Deng
2020
Wang al. 2024).
Simplifications over Existing Iterative Methods We replace cost volumes, which are standard in existing iterative methods (Sun et al. [0188 Teed & Deng. 2020 |Hang et al., 2022) Wang eet al., 2024). with high-resolution warping, which more memory-efficient In addition, we have removed the context encoder (Sun al. 2018a), another flow-specific design standard in existing
iterative methods.
A direct benefit from our simplified design that load pre-trained weights for standard archi- tectures such as ViT (Dosovitskiy et al. 2020), which can improve generalization as our experiments will show Section 5.5
The simplicity of our meta-architecture also enables more apples-to-apples comparisons between direct methods and iterative methods. Existing direct methods (Weinzaepfel et al. 2022 2023 Saxena et al., 2024) share an input format similar to that of the first iteration of WAFT, making them more directly compatible. We will empirically show the effectiveness and necessity of iterative indexing within our meta-architecture in Section 5.5
5
EXPERIMENTS
We report results on Sintel (Butler et al.
2012). KITTI (Giiger et al. 2013). and Spring (Mehl et al.)
2023). Following existing work (Teed & Deng 2020 Huang et al. 2022 Wang et al. 2024 Morim-
itsu et al. 2025). for training, we use ElythingChairy Dosovvissiiyyyooo et al. 2015). ). Flying Things (Mayer
al. 2016). HDIK (Kondermann et al. 2016). Sintel (Butler et al. 2012), KITTI (Geiger al.
2013), Spring (Mehl et al. (203)) and TartanAir (Wang el. 2020). We use the widely adopted met- rics: endpoint-error (EPE). 1-pixel outlier rate (Ipx), percentage of flow outliers (FI), and weighted area under the curve (WAUC). Definitions can be found in (Richter et al.) 2017 Mehl et al.) 2023
Geiger et al. 2013
Morimitsu al. 2025
5.1 ARCHITECTURE DETAILS
Input Encoder We use frozen ImageNet- pretrained Twins-SVT-Large (Chu al. 2021),
Methed
1px.
WAUC!
FlowNet2 [Il et 2017
6.710
1.040
2.823
90.907
depth-pretrained DAv2-S (Yang et al." 2024).
SpyNet (R RRmn Bloc 2017
29.963
4,162
12,866
67.150
PWC- Ner
4.899
and wawpeerviied--petaainee DINOv3-VIT.
6.790
1.476
3.198
S (Siméoni et al. 2025) in input encoders.
GMA Tiing di PJ/T
7.074
0.914
3.0 3,079
90.722
HowParmer (iliinn
2023
6.510
0.723
2. 2.384
91 679
GMFlow Xe
10.355
2,952
Recurrent Update Module We use modi- fied DPT-Small (Ranftl et al.] (02TI) as the re- current update module. We concatenate the im- age features and use
CroCoFlow (Weinzappld et
RPKNet Moriiiaa dlai
2024
2023
4.565
4.809
0.198
0.657
1,756
L.508
93 660
638
Win. Win Leoyera
5.371
0.475
1,621
92.270
SEA-RAFTIMII
DPFlow Morimlis dr 20las
3,442
0.340
1.311
94.980
1 conv to obtain the initial hidden state. Since the image features are already 2x downsampled, we change the patch size to 8. We set the resolution of the positional embedding to 224 x 224, and interpolate it for other resolutions. We use 5 iterations in training and inference.
SAPT-DAY-aI-SSO
3.418
0.340
1.280
64.633
WAFT-IM-2-a1-1000
3.268
94.
WAPT-DAv2-a2
3.298
0.304
1.197
94,990
WAFT-DINOV342
3.182
0.325
1.346
95,051
Table 3: WAFT ranks 1st on Spring Mchl et al. on all metrics. We highlight all SOTA performance. denotes the submissions from the Spring team.
2023

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Sintel
KITTI
Inference Cost
Type
Method
Clean,
Finall.
Alll
Non-Oeel
#MACs (G)
Lalency (ms))
GMFlow (Xu et all
2022
1.74
2.90
9.32
3,80
603
139
Direct
CroCoFlow Weinzacptel al. 2023
1.09
2.44
3.64
2.40
57343
6422
DDVM Saxena al.
2024
1.75
2.48
3.26
2.24
PWC-Net+ (Sun et 2019
3.45
4.60
7.72
4.91
101
24
RAFT [Teed Dong
2020
5.10
3.07
141
DIP Zhenng el
2022
1.44
2.83
4.21
2.43
3068
499
GMPlowNet Zhao a
2022
1,39
2.65
4.79
2.75
1094
244
CRAFT (Sui сt a. 2022
1.45
2.42
4.79
3.02
2274
483
Former (Hulan al.
2022
1.20
2.12
4.68
2.69
1715
336
GMFlow+ e0 20/51
250
Iterative
RPKNel Monnnisu
2024
1.31
2.65
4.64
2.71
137
183
w/ Cost Volume
CCMR+ Jahed et 2024
1,07
2.10
3,86
2.07
12653
999
MatchFlow(()) Dong et
2023
1.16
2.37
4.63
2.77
1669
291
Flowformer+++ (Shi
2023
1.07
1.94
4.52
374
SEA-RAFT(L))
2024
1.31
655
108
AlyFl (Jung èt
M024
1,23
2.44
4.41
2.69
Flow FlowDiffuser (Luo al 2124
1.02
2.03
4.17
2.82
2466
599
SAMFlow Zhou cl 2024
1.00
2.08
4.49
9717
1757
DPFlow Morimitsu
2125
1.04
3.56
2.12
414
131
SpyNet (Ranjan Black
2017
6,64
35.07
26.71
25
FlowNed2 (ilg 2017
4.16
5.74
10.41
6.94
230
75
Iterative
WAFT-DAiZ-al
1.09
2.34
3,42
2.04
853
240
w/ Warping
WAFT-Twins-22
1.02
2.39
3.53
2.12
1020
290
WAFT-DAv2-a2
0.95
2.33
3.31
2.03
807
240
WAFT-DINOv3-12
0.94
3.56
2.13
732
212
Table 2: We report endpoint-crror (EPE) Sintel (Butler 2012 Fl KITTI (Geiger al. 2013), and
highlight all SOTA performance. On KITTI, WAFT ranks first on non-occluded pixels and second on all pixels. It also achieves state-of oftthoof-theart performance on Sintel (clean). We measure the latency on an RTX3090 with batch size and 540p input.
5.2 TRAINING DETAILS
Benchmark Submissions Following SEA-RAFT (Wang et al. 2024), we first pre-train our model on TartanAir (Wang et al. 2020) for 300k steps, with batch size of 32 and learning rate 4 x 10-4 We fine-tune our model on FlyingChairs (Zhao et al. 2020a) with the same hyperparameters for 50k steps, and then fine-tune it on FlyingThinge (Myerr et al. (016) for 200k steps. For all submissions, we keep the batch size as 32 by default and reduce the learning rate to 10 For KITTI (Geiger et al, submission, we fine-tune our model on (trii( for 5k steps. For Sintel (Butler et al,, 2013)
2012
submission, we follow previous work to fine-tune our model on the mixture of FlyingTThing
HD
(Kondermann et al.) 2016). KITTn(rrain), and Stntel(train) for 200k steps. For Spring (Mchl 2023) submissions, we fine-tune our models on Spring(train) for 200k steps with a batch size We train an extra 1080p WAFT-DAv2-al model with batch size of 8.
al.
of 32.
Zero-Shot Evaluation We first train our model on FlyingChairs for 50k steps, and then fine-tune it FlyingThings for 50k steps. The batch size is set 32, and the learning rate is to 10-4.
5.3 BENCIMARK RESULTS
Sintel & KITTI Results are shown in Table 2 Using Twins backbone only pre-trained on Im- ageNet, WAFT ranks second on KITTI and is competitive on Sintel (clean). It outperforms prior cost-volume-bast based SOTA Flowformer++ (Shi et 2023) in both accuracy and efficiency given the same backbone, demonstrating the strength of high- further improved with stronger backbones (Yang al. 2024, Siméoni et al. (025). Using depth- pretrained DAv2 (Yang al.) 2024). on KITTI (Geiger al. 2013), WAFT achieves the best Fl on non-occluded pixels and the second best on all pixels. It also ranks first on Sintel (Clean) (Butler et al. 2012) and competitive have competitive accuracy (e.g.. 1.3x than Flowformer++ Shi et al. (2023). 4. than CCMR+ Ja hedi et al. (2024)). demonstrating its high efficiency.
warping. The performance can be
Sintel (Final). WAFT is 1.3-4.1x faster than existing methods that
Note that there an outlier sequence, 'Ambush 1', which severely affects the average performance on Sintel (Final) mentioned in Saxena et al. (2024). We show that WAFT outperforms Flow-

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Published as a conference paper at ICLR 2026
former++ Sintel (Final) with the same Twins backbone when 'Ambush 1' is excluded. More details can be found in Table 6
Spring Results are shown in Table Following the downsample-upsample protocol (540p) of SEA-RAFT (Wang et al. 2024). WAFT outperforms existing methods on EPE and 1px with a Twins backbone only pre-trained on ImageNet. We also show that WAFT achieves the best performance on all metries with a peeett-pperrieeed DAv2 backbone. Benefiting from warping, WAFT can be trained at full resolution (1080p) to improve the performance further.
Comparison with Existing Warping-based Methods It appears that warping network op- cration has been largely abandoned by works in the last years and the last time warping-baa -bsse methods achieved top positions the leaderboards
around 2017 (Ranjan & Black 2017 Ig et al. 2017). WAFT is significant in that it has revisited and revived an idea that has fallen out of favor. Compared methods that do use warping (Ranjan & Black 2017; 1ig al. 2017). WAFT
reduces endpoint-error (EPE) by at least 64% on Sintel and 70% on Spring (Mehl et al. 2023). while
also reducing Fl by at least 68% on KITTI (Geiger et al. 2013) and 57% on Spring (Mehl et al. 2023). Besides, WAFT reduces pux-iullier rate by 52% on Spring (Mchl et al. 2023
5.4 ZERO-SHOT EVALUATION
Sintel (train)
KITTI (train)
Method
Clean]
Final]]
Fl-epe]
Fl-il]]
Following previous work TTedd & Deng 2020 Huang et al. 2022 Sun et al. 201 we train our model on TlynnChhiry (Dosovitskiy et al. 2013) and TlyingThing (Mayer et al. 2016]. Then we evaluate the performance on the train- ing split of Sintel (Butler et al. 2012) and KITTI (Giggrr et al. 2013).
PWC-Ner
33,7
RAFT Tetd & Dele 2020
1.43
2.71
5.04
17.4
GMA
1.30
2.74
4,69
17.1
SKFlow SSun ol
1.22
2.46
4.27
15.5
DIP (Zhei dl F11
1.30
2.82
4,29
13,7
EMD-L
CRAFT SSill et sil Fuzze
1.27
2.79
4,88
17.5
RPKNet (Menmmss
R02
1.12
2.45
13.0
GMFlow Zii
Flow
Huinng 2 RUzA
1.01
2.40
4,09
14,7
Flowformer
0.90
2.30
3,93
14.2
CCMR+ Jaheda el MUA4)
0.98
2.36
12,9
Analysis Results are shown in Table WAFT achieves strong cross-dataset general- ization. On KITTI (train), WAFT outper- forms other methods by a large margin with an ImageNet-pretraing proves the endpoint-error (EPE) from 3.37 to 2.98 and FI from 11.1 1 to 9.9. On Sintel (train), WAFT achieves performance close to state-of- the-art methods. Compared to the previous warping-based method (lg et al. (2017), WAFT improves the performance by at least 31%.
MatchFlow(()
Diing ci dlll 2023
1.03
2.45
4,08
15.6
SEA REA-RAPTLL
1.19
AnyPlow (Jang 2 ni
2024
1.10
2.52
3.76
12.4
SAMFlow Zoou
2012
0.87
2.11
3.44
12.3
DiowDiff (raved 21144
0.86
2.19
3.61
11.8
DPFlow (Modinitsu ai 2012
1.02
2.26
3.37
11.1
Twins backbone: It im-
FlowNer2 (ll etal. 0alal..2000 2017
2,02
WAFT-DA WAFT-Twins-a2 WAFT-DAv2-22
1.00
2.15
3.10
10.3
1.02
2.46
2.98
9.9
1.01
2.49
3,28
10.9
WAFT-DINO13-22
1.28
2.56
3.49
12.9
Table WAFT achieves best cross-dataset general- ization on KITTI(train), reducing the error by 11%. We highlight all SOTA performance.
5.5 ABLATION STUDY
We conduct zero-shot ablations in Table 5 on the training split of Sintel (Butler et al. 2012) and the sub-val split (Wang al.) (2024) of Spring (Mehl et al.) (2023) based on WAFT-DAv2-al. In all experiments, the models are trained on Flything Things (Mayer et al. (0066) for 50k steps, with a batch size of 32 and learning rate 10 The average EPE and 1px reported.
Different Input Encoder Both pre-trained weights and adaptations are important to the perfor- mance. Note that the strong performance of WAFT is not merely from advanced backbones. Using a Twins backbone only pre-trained on ImageNet as adopted in Flowformer (Huang et al. 2022 WAFT ranks first on Spring, second on KITTI, and is competitive on Sintel. More details can be found in Table and
Different Recurrent Update Module The vision transformer design is crucial to iterative warp- ing. We observe significant performance drop when replacing the DPT-based recurrent update module with CNNs, highlighting the importance modeling long-range dependence. This finding may help explain why early deep learning approaches (Ilg al. 2017 Ranjan & Black 2017) that
8

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Published as a conference paper at ICLR 2026
Index Reso.
Sintel(train)
Spring(sub-val)
Experiment
Inpur Enc.
Rec. Upd.
#Steps
#MACs
Clean
Finall
EPR]
!px4
WAFT-DAv2-al
DAv2-S+Res18
DPT-S
1/2
1.18
2.33
0.27
1.43
858G
Res18
1.27
2.81
0.27
1.59
600G
Different input enc.
DPT-S
1/2
DAv2-S
1,55
2.64
0.37
2.70
670G
DAv2 w/o pre-train
DAv2-S+Res18
DPT-S
1/2
1.42
2.74
0.28
1.77
858G
Res18
6.84
2.93
1098G
Different rec. upd.
DAv2-S+Res18
5
1/2
COnvGRU
2.79
4.80
0.39
2.71
800G
1/8 reso. + warp
DAv2-S+Res18
DPT-S
1/8
1.15
2.31
0.32
1,82
859G
1/8 reso. corr
DAv2-S+Res18
DPTSS
1/8
1.10
2.45
0.33
1.74
883G
DPTS
2.36
3.43
0.59
10.5
1009G
Direct variants
DAv2-B+Res18
1/2
DPT-B
2.37
3.38
0.61
11.1
1277G
Image-space warp
DAv2-S-Res18
DPT-S
1/2
1.28
2.50
0.27
1.37
1902G
Refine w/o warp
DAv2-S+Res18
DPT-S
1/2
2.04
3.42
0.58
9.44
858G
w/ Context
DAv2-S+Res18
DPT-S
1/2
1.22
2.32
0.29
1.70
1005G
Table 5: We report the shot ablation results Sintel(train)) (Butler 2012
and Spring(sub (Mehl changes can be identified through comparisons with the first row.
2003 Wang al. (2024). The effect
See Section 5.5 for details.
implemented warping using CNNs underperformed compared to cost-volume-based methods (Sun al. 2018a Teed & Deng 2020 Huang al., 2022).
High- Regh-Resolution Indexing High-resolution indexing into feature maps using current flow esti- mates remarkably improves performance. We implement variants that index at 1/8 resolution by changing the patch size of DPT 2, and find that high-resolution indexing significantly im- proves 1px-outlier rate on Spring(sub-val) (Mehl et al. 2023).
We also design a cost-volume-basse variant following the common setup (Wang et al. 2024 Teed & Deng] 2020) HHang et al., (002)) with look-up radius 4 at 1/8 resolution, and find that it performs similarly to the warping counterpart (shown Table 5) but costs 2.2x training memory (21.2 2 GiB
vs. 9.5 GiB).
Direct vs. Iterative Iterative updates achieve better performance than direct regression within meta-architecture. We implement direct regression by setting the number of iterations T = For fair comparison, we scale up the networks to match the computational cost of 5-iteration WAFT. Our results show that WAFT significantly outperforms these direct regression variants, indicating the effectiveness and high efficiency of the iterative paradigm. This finding aligns with the obser- vation that existing direct methods either underperform (Xu et al.
2022
Weinzaepfel et al. 2022)
require substantially
computational cost (Saxena et al.
2024
Weinzaepfel al.
2022
2023) compared to iterative approaches (Teed & Deng 2020 Wang et al. 2024 Huang et al.
2022
Morimitsu et 2025).
Warping Features vs. Pixels Feature-space warping is more effective than image-space warping.
which is commonly used in classic methods (Ma et al. 2022 Brox et al. 2004 Black & Anandan, 1996) and early deep learning methods (ll al. 2017; Ranjan & Black 2017). Feature-space warping does not need to re-extract features for the warped image in each iteration, significantly saving computational cost while achieving slightly better accuracy.
Effectiveness Warping It possible to perform iterative updates without warping the features using the current flow estimates. We can simply use the original feature maps input to the update module. Compared to this baseline, warping has significantly lower error with a negligible cost. This observation aligns with the conclusions of previous work (Brox al.) 2004), which theoretically justifies the combination of warping and recurrent updates by framing as a fixed-point iteration algorithm.

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Published as a conference paper at ICLR 2026
Context Encoder Prior work (Sun al. 2018a Teed & Deng 2020 Wang al. 2024 Huang et al. 2022) has often used a context encoder that provides am extra imput to the update module. Our ablation show that the context encoder is not necessary. The context encoder introduces additional computation overhead, but does not significantly affect performance. Previous work (Wang et al, (222)) also points out that the context encoder can be regarded direct flow regressor, which functions similarly to the first iteration of WAFT.
ACKNOWLEDGMENTS
This work was partially supported by the National Science Foundation.
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Sequence
WAFT-TWins-a2
DPFlow
Flowformer++
FlowDiffuser
DDVM
SAMFlow
Perturbed Market 3
0.893 (0.460)
0.892 (0.423)
0.958 (0,511)
0.897 (0.490)
0.787 (0.372)
0,932 (0.493)
Perturbed Shaman
0.174 (0.163)
0.213 (0.201)
0.251 (0.236)
0.267 (0.255)
0.219 (0.196)
0.241 (0.219)
Ambush 1
20.561 (2.526)
8.366 (2.970)
6.610 (2.605)
7.224 (2.583)
29.33 (14.07)
10.586
Ambush 3
3.173 (2.048)
(1.729)
2.939 (1.816)
3.148 (1.828)
2.855 (3.016)
3.411 (1.779)
Bamhoo 3
0,460 (0.423)
0.486 (0.438)
0.546 (0.513)
0.594 (0.508)
0.415 (0.380)
0.522 (0.473)
Cave
2.199 (1.567)
2.341 (1.631)
2.344 (1.477)
2.464 (1.433)
(1.658)
2.475 (1.445)
Market 1
0,851 (0.384)
0.890 (0.491)
1.073 (0.550)
1.238 (0.517)
0.719 (0.467)
1,060 (0.491)
Market 4
7.513 (3.933)
7,939 (3.834)
8.086 (4.450)
8.024 (4.027)
5.517 (3.971)
6.636 (3.680)
Mountain
(0.087)
(0.078)
0.288 (0.118)
0.409 (0.101)
(0.095)
(0.217)
Temple
0.511 (0.297)
0.511 (0.302)
0.657 (0.359)
0.789 (0.340)
0.452 (0.284)
0.853 (0.340)
Tiger
0.463 (0.333)
0.571 (0.411)
0.595 (0.430)
0.636 (0.391)
0.413 (0.344)
0,573 (0,392)
Wall
1.708 (0.979)
2.011 (1.231)
1.727 (0.793)
1,692 (0.734)
1.639 (3.210)
2.105 (0.780)
Avg
2.393 (1011)
1.955 (1.046)
1993 (1.073)
2.026 (1.016)
2.475 (1.754)
0.995 (2,080)
Avg (w/o Ambush 1)
1.639 (0.952)
1.710 (0.966)
1,750 (1.010)
1.810 (0.951)
1,360 (1.242)
1.727 (0.923)
Table 6: We report the endpoint-error (EPE) on all sequences of Sintel (Butler et al. 2012). shown in the format "final-epe (clean-epe)". We highlight the best result on each sequence.
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View synthesis with sculpted neural points.
arXiv preprint
A APPENDIX
Sintel Results We show the sequence-wiss results of several representative methods (Morimitsu et al. 2025 Huang et al., 2022 Luo et al., 2024; Saxena et al.) 20044 Zhou al., 2024) on Sintel in
Table 6 The sequence 'Ambush I' appears to be an outlier which severely affects the average EPE on the final split. Given the same ImageNet-pretrained Twins backbone, WAFT outperforms SOTA Flowformert+ when 'Ambush I' is excluded.
14


Reviews:
[{"review_id": "CUraorfbdT", "reviewer": "Reviewer_ZeZq", "summary": "This paper proposes to abandon the cost volume that's a standard component in deep optical flow architectures, and uses the warped target feature vector alone (plus the source feature vector) for flow estimation. It implicitly builds a global context via self-attention in the recurrent update module, which is why it can get rid of the cost volume.", "questions": "1. Since no cost volume is adopted, I'm worried that the initial errors may accumulate and become larger as the model iterates. The authors could consider such a perturbation test: in the first iteration, perturb the flow prediction with random values, then see how well the model recovers from it.\n2. Another challenging scenario is if there are multiple similar objects (e.g. a table with many cups), how well WASP would perform. Chance is the self attention may overly smooth features across these similar objects and make the prediction more random. Of course this would be challenging for methods **with** a cost volume, but I'm curious if it would be more challenging for WASP.\n\n (Note: I would not lower my rating if the model is not so robust under such perturbations; this is just to better inform readers whre are the \"sweet spots\" in which the method performs well.)", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. Removing the cost volume is a good contribution, which may make estimation of optical flow on high-res images much more feasible.", "weaknesses": "1. No detailed evaluation of how the model performs on large displacements, on which WAFT might be slightly weaker than models using a cost volume. The authors could make artificial displacements to stress-test WAFT to see where its limit lies.\n2. No details are given for the Recurrent Update Module. For example, how many layers (esp. self attention layers), what's the total param count?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "lZWXgYLKrC", "reviewer": "Reviewer_oRyW", "summary": "This paper points out the drawback of constructing cost volume in the optical flow field, and propose to replace the cost volume with warping. To achieve competitive performance, the authors propose to utilize: 1) stronger feature encoder 2) high-resolution warping 3) attention- (and pretrained) based updater.", "questions": "Please refer to the Weaknesses section.", "limitations": "", "rating": 8, "confidence": 5, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "1. This paper is well written.\n2. This paper has a clear and extensive ablation study to show the effectiveness of each design choice.\n3. The author introduced an attention-based updater to replace the cost volume for feature similarity computation. This design is resonable and novel.", "weaknesses": "1. Since the author replaces the commonly used CNN updater with attention-based one, it is better to provides more details of the layers.\n2. For the models used in Table 2, what is the downsampled ratio? And which line corresponds to the statement in the abstract \"while being up to 4.1× faster than methods with similar performance\". From my understanding, WAFT-Twins-a2 uses the same feature encoder as FlowFormer++, achieves similar performance but not significant speedup?\n3. Can the authors provide more explaination about why context encoder is not useful in the WAFT architecture?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "FJe8nkLrSP", "reviewer": "Reviewer_abBA", "summary": "This paper proposes a simplified meta-architecture for optical flow estimation without using cost volumes. The proposed WAFT algorithm consists of an input encoder which leverages existing large-scale pre-trained models for feature extraction, and a recurrent update module based on vision transformers that can iteratively updates optical flow with large displacements. Experiment shows that the proposed WAFT algorithm achieves top rankings on various benchmarks, including Spring and KITTI, furthermore, it does so with significantly lower memory cost and up to 4.1x faster inference times.", "questions": "1. In table 2, while the ratio of MACs reduction is high (CCMR+'s 12653 vs. WAFT-DAv2-a1's 853), the speed up of latency is not at the same scale (CCMR+'s 999 vs. WAFT-DAv2-a1's 240), it would be good to give further explanation on this.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The design of WAFT without cost volume computation is very simple, flexible and effective, making it a significant contribution for computer vision research community\n2. By avoiding cost volumes computation, WAFT can perform warping on original resolution feature maps, which can help achieving sharper boundary predictions in optical flow estimation\n3. WAFT has shown best zero-shot cross-dataset generalization on KITTI, which is an important property towards generalization capability on unseen data.\n4. The paper is well-structured and clearly written.", "weaknesses": "1. The iterative recurrent update module may restrict the algorithm's potential for parallel optimization to achieve low latency. \n2. WAFT relies on existing pre-trained vision foundation models, which may limits its potential for further computational efficiency improvement on feature extraction.\n3. Compared with improved memory and computational efficiency improvement, the improvement on flow accuracy is relatively limited.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Oral)