ARFlow: Auto-regressive Optical Flow Estimation for Arbitrary-Length Videos via Progressive Next-Frame Forecasting
Authors: Jiuming Liu
Mengmeng Liu
Siting Zhu
Yunpeng Zhang
Jiangtao Li
Michael Ying Yang
Francesco Nex
Hao Cheng
Hesheng Wang
Venue: ICLR 2026 Poster (2026)
Keywords: Optical Flow Estimation
Primary area: applications to robotics, autonomy, planning

Abstract: Optical flow estimation is a fundamental computer vision task that predicts per-pixel displacements from consecutive images. Recent works attempt to exploit temporal cues to improve the estimation performance. However, their temporal modeling is restricted to short video sequences due to the unaffordable computational burden, thereby suffering from restricted temporal receptive fields. Moreover, their group-wise paradigm in one forward pass undermines inter-group information exchange, leading to modest performance improvement. To address these problems, we propose a novel multi-frame optical flow network based on an auto-regressive paradigm, named ARFlow. Unlike previous multi-frame methods, our method can be scalable to arbitrary-length videos with marginal computational overhead. Specifically, we design an Auto-regressive Flow Initialization (AFI) module and an Auto-regressive Multi-stride Flow Refinement (AMFR) module to forecast the next-frame flow based on multi-stride history observations. Our ARFlow achieves state-of-the-art performance, ranking 1st on both KITTI-2015 and Spring official benchmarks and 2nd on the MPI-Sintel (Final) benchmark among all open-sourced methods. Furthermore, due to the auto-regressive nature, our method can generalize to arbitrary video length with a constant GPU memory usage of 2.1GB.

Published as a conference paper at ICLR 2026
ARFLOW: AUTO-REGRESSIVE OPTICAL FLOW ESTI- MATION FOR ARBITRARY-LENGTI VIDEOS VIA PRO- GRESSIVE NEXT-FRAMM FORECASTING
Jiuming Liu'x, Mengmeng Liu Siting Zhu', Yunpeng Zhang', Jiangtao Li', Michael Ying Yang', Francesco Nex², Hao Cheng"', Hesheng Wang'1 Shanghai Jiao Tong University University of Twente 'Phigent Roboties *University of Bath fuu fijiinnggl 2 gma com, m.
ABSTRACT
Optical flow estimation is fundamental computer vision task that predicts per- pixel displacements from consecutive images. Recent works attempt to exploit temporal cues improve the estimation performance. However, their temporal modeling is restricted to short video sequences due to the unaffordable computa- tional burden, thereby suffering from restricted temporal receptive fields. More- over, their group-wise paradigm in one forward pass undermines inter-group infor- mation exchange, leading modest performance improvement. To address these problems, we propose a novel multi-frame optical flow network based on an auto- regressive paradigm, named ARFlow. Unlike previous method can be scalable to arbitrary-length videos with marginal computational overhead. Specifically, we design an Auto-regressive Flow Initialization (AFI) module and an Auto-regressive Multi-stride Flow Refinement (AMFR) module to forecast the next-frame flow based on multi-stride history observations. Our ARFlow achieves state-of-the-art performance, ranking 1st on both KITTI-2015 and Spring official benchmarks and 2nd on the MPI-Sintel (Final) benchmark among all soeresourced methods. Furthermore, due to the auto-regressive nature, our method can generalize to arbitrary video length with a constant GPU memory usage of 2. GGB
methods, our
INTRODUCTION
Optical flow indicates 2D displacement field each pixel predicted from consecutive video frames, playing key role in various downstream applications such as video inpainting (Xu et al.)
2019 Li al. 2022), dynamic scene reconstruction (LUU et al. 2025 Zhu al. 2024, Deng al,, 2025b
Chan et al. 2002; Li et al. 2023). and video generation Liang et al. 2024a b).
With the development of advanced model architectures (Deng et al.
2025c), optical flow estimation
has witnessed remarkable success, evolving from CNN-based backbones (Sun et al. 2018 Teed &
Deng 2020) to transformer- based ones (Huang et al.) [022 Xu et al. 2022); from discriminative
methods (Zhao al.] 2024) to generative ones (Luo al. [202||
et al. [023]] Liu et 2202aa]
2025ch. However, these methods typically formulate the task with a pairwise paradigm, which
neglects intrinsic temporal coherence within video sequences, leading suboptimal performance
and poor reasoning ability in occluded areas (Chen et al. 20233 Dong & Fu 2024 Liu et al. (223c)).
Recently, there has been an increasing research focus multi-frame optical flow estimation from video sequences (Shi et al. 2023a Sun et al.) 2024] Bargatin et al. 2025 Dong & Fu (024). as observe that concurrent multi-frame methods commonly Tail to exploit sufficient temporal cues because of the following: (1) Limited temporal receptive fields: Ex- isting multi-frame settings typically segment the entire video into multiple groups with a fixed size, e.g., three frames in MemFlow (Dong & Fu 2024) (Fig. [ (A)), four frames in StreamFlow (Sun)
shown in Fig. D However,
al. 2024 (Fig. (B). This group-wise segmentation fails to capture long-range dependencies
"These authors contributed equally. 1 !Coresponding authors: Hao Cheng, Hesheng Wang.

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Published as a conference paper at ICLR 2026
lu
I
Memory Bank
Memory Bank
F
GRti-based Flow Refinement
F Fets
F Furl
(A) Many-to-one multi-frame pipeline
fu
1-
Multi-stride Temporal Weighting
F 19!1
F
FoLL
Memery Update
Fair
Fu-t
FuLes
Fezs-
F
FIN43
(B) Dequence-to-sequelle multi-frame pipeline
(C) Auto-regressive multi-frame pipeline (Ours)
Figure 1: Comparison with previous multi-frame optical flow methods.
throughout the total video length. (2) Heavy computational overhead and memory usage: Vide- oFlow (Shi et al. 2023a) inputs overlapping clips estimate multiple flows. MemFlow (Dong & Fu, 2024) utilizes many image inputs to strengthen the current estimation. Both approaches repeat feature extraction and context encoder several times different groups, impeding real-time appli- cations. StreamFlow (Sun et al., 2024) suffers from high GPU memory usage, shown in Fig. 2 This inefficiency further leads to poor scalability, allowing only a limited number of frame inputs. (3) Lack of multi-stride temporal modeling: Prior methods typically focus single-stride temporal modeling with short-term motions. However, both long-range and short-term motions are crucial for optical flow: long-range motions facilitate recognizing occlusions and out-of-boundary pixels across frames (Shi et al. 2023a), while short-term motions capture subtle variations in adjacent frames.
To address these problems, propose a novel multi-frame optical flow estima- tion pipeline (ARFlow) using auto- regressive prediction paradigm in Fig. [
Memory Usage Comparison
Mamory Usage (GB)
(C). Specifically, Auto-regressive Flow
Initialization module (AFI) is developed retrieve multiple history flow estimates from the memory bank and forecast the next-frame initial flow based on the his- tory ones. This process will introduce an accurately initialized flow, which sig- nificantly improves the performance and also decreases the iteration numbers subsequent refinement modules, enabling higher efficiency. After obtaining the ini- tial flow, another Auto-regressive Multi-
35
10.1
23
Frame Length
Figure 2: Previous group-wise multi-frame methods suffer from significant memory usage increase with longer frame length. In contrast, method maintains nearly constant GPU consumption.
stride Flow Refinement module (AMFR) is also designed by incorporating multi-granularity tem- poral information from diverse intervals to combine both long-term and short-term motions. Unlike previous multi-frame works that segment videos into multiple groups, we utilize the entire video sequence as the network input, where sequential images are delivered frame by frame. When new frame is input, its features are extracted and correlated with previous history flows to predict the current flow. This paradigm breaks the limitation local receptive fields, possessing strong scal- ability to arbitrary frame lengths with no memory usage increase in Fig. 2 Please refer to the supplementary videos for scalable optical flow estimation on arbitrary-length inputs.
Overall, the main contributions of our method
follows:
We design novel multi-frame optical flow estimation method, named ARFlow, based on the auto-regressive paradigm. Through progressively forecasting the next-frame flow, our method can be scalable to arbitrary-length videos.
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An Auto-regressive Flow Initialization module (AFI) is developed to retrieve multiple his- tory flows from memory banks and forecast the next-frame initial flow based on these history estimates.
An Auto-regressive Multi-stride Flow Refinement module (AMFR) is also designed to fur- ther refine the initial flow by incorporating both long-term and short-term temporal cues.
Extensive experiments on MPI-Sintel (Butler et al. 2012), 2),, Spring (Mehl et al. 2023b), and KITTI-2015 (Geiger et al. 2013) datasets demonstrate the state-of-the-ar -art performance of our proposed ARFlow. Moreover, due to the accurately predicted initial flows, the num- ber of iterations in the following refinement module is decreased, enabling lower memory usage compared with prior multi-frame methods.
RELATED WORK
Pairwise Optical Flow Estimation. Traditional methods (Black& & Anandan, 1993, Bruhn et al.,
2005 Horn& & Schunck 1981) typically formulate optical flow estimation as an optimization prob- lem which maximizes the visual similarity between two frames. FlowNet (Dosovitskiy et al. 2015) regresses the optical flow with Convolutional Neural Networks (CNNs) in end-to-end manner. PWC-Net (Sun et al.) 2018) proposes a Pyramid, Warping, Cost volume method for coarse-to- fine flow estimations. To address the problem of small fast-moving objects, RAFT (Teed & Deng. 2020)
designs a recurrent iteration module to improve estimation accuracy. Subsequent works (Ranjan &
Black 2017 Hui et al. 2018 Yang & Ramanan 2019 Hui al., 2020; liang et al., [021]] Zhang
et al. 2021] Sun et a 2022 Jahedi et (0024 Wang et al. 2024b) progressively refine model ar-
chitectures in a coarse-to to- fine or iterative manner. Various transformer-basse
methods (Huang et al.) Shi ct al. 2023b; Xu et al., 2022; Liu et al. 2023b] (Shan et al.) 2021: Sui et al.) 2022 Luo 2023) are then developed to enlarge the matching receptive field. Some methods Liu et al.
2022
ct al.
[2022
2023a) also incorporate scene flow estimation Jiang et al. (2024); Liu et al. (2025d); Zhang
et al. (2024): Liu et al. (2025b); Zhang et al. (2025): Liu et al. (2024c b): Feng et al. (2024) by joint
optimization. Nevertheless, these two-frame optical flow methods fail to take intrinsic temporal cues
across frames into consideration.
Multi-Frame Optical Flow Estimation. The research for multi-frame flow estimation has gained remarkable advances in recent years. RAFT (Teed & Deng. 2020) utilizes a "warm-start" strategy, which initializes the current flow by warping previous flow estimates. Some self-supervised meth- ods (Liu et al., 2019 Hur & Roth (021) are also proposed to retrieve temporal information by CNN or LSTM. VideoHlow (Shi et al. 2023a) calculates the bi-directional flows with a three-frame setting and designs motion propagation module to exchange information across different triplets. Splat- Flow (Wang et al., 2024a) introduces differential splatting transformation to align motion features from previous timestamps. StreamFlow (Sun et al. 2024) proposes an in-batch estimation strategy, simultaneously estimating all successive flows. More recently, memory mechanisms (Dong & Fu, 2024 Bargatin al., 2025) have been introduced to store previous flow-related features and further improve the current estimation. However, these prior filti-frame methods can only process seg- mented groups with limited sizes (3-5 frames), impeding sufficient temporal modeling. Also, they suffer from heavy computational burdens and memory usage with increasing video lengths. In this paper, our ARFlow resorts to the auto-regressive transformer by progressively forecasting the next frame's 's motion, which is scalable to arbitrary video lengths while keeping low memory usage.
Auto-regressive Video Generation. Recently, auto-regressive models benefiting from scaling laws (Henighan et al. 2020) have gained great success in the video generation field (Xie et al., 2025 Yin
al. 2025 Tang et al. 2024, Zhou al. 2025 Henschel al. 2025 Zhai et al. 20255 Huang 2025| Liu et al. 2025a] Deng et al. [025a] Liu et al. 2025c Ge et al.) 2026 Xu et al. 2024).
et al.
PA-VDM Xie et al. (2025) proposes a progressive noise-adding mechanism and denoises the frame using small intervals. VID Yin et al. (2025) adapts a pre-trained bidirectional transformer and designs a distribution matching distillation technique to reduce latency. Self Forcing Huang et al. 2025) presents the auto-regressive rollout with key-value (KV) caching during training. Recent studies have also emphasized temporal structure and -orizon dependencies for dynamic-scene understanding (Liu et al. 2025f 2024d 2023d Cheng al. 2023b a) motivating an regressvee formulation for optical flow. Inspired by the great success in these auto-regressive video generation works, we, for the first time, introduce the auto-regressive paradigm into optical flow estimation.
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Memory Bank
Auto-regressive Flow Initialization
Toea Trea Trmal
Temporal Transformer
Forecast
I+1
F--5.t-t
(stride 1)
GRU Iteration
Tranef
fx+11
AfR cf 2217
Fr413
Auto-regressive Multi-stride Flow Refinement
Temporal Transformer (stride 2)
Forecast
Thack
Multi-stride Temporal
Temporal Transformer (stride +)
Forecast
Weighting
Fe201
Trmaa
Chea
FC t++1
Memory Update
Fun1
FELt
Figure 3: The overall architecture of our proposed ARFlow.
3 METHOD
3.1 OVERALL ARCHITECTURE
As in Fig. (C), the input of our network video sequence 11, 11.1,11111111 where 4 E HxW x2 indicates the image the timestamp f. H, W are the height and width of the input images. The outputs are corresponding sequential optical flows F1.2, F2.3, .... FL-1,L between consecutive image pairs, where Fr,++1 indicates the optical flow from I, to Tt+1-
In our auto auto-regressive setting, sequential images are delivered into the network frame by frame. When new frame I++1 inputted into Fig. 3 forecasted flow initialization ft,441 first generated based previously predicted T frames' history flows stored in the memory bank in Section 3.2 Then, above initial flow ft,++1 iteratively refined by incorporating intermediate context and motion features from I to I++1 and multi-stride forecasted flows in Section 3.3 The output refined flow finally used to update the memory bank and forecast the following flow fi+1.42 in an auto- regressive manner. The network is supervised by the loss functions defined in Section 3.4
3.2 REGRESEEGES FLOw INITIALIZATION
Motivated by prior works from Xu et al. 2023a), long-range temporal dependencies are crucial for optical flow estimation, where potential challenges such as occlusions be alleviated utilizing multi-frame temporal cues. In addition, optical flows for dynamic objects tend to be consistent across frames. Therefore, we history flow estimates from a designed memory bank to forecast an extrapolated flow in the next frame for enhanced flow initialization.
Memory Bank. When a new pair images (I, and 4+1) delivered to the network as in Fig. 3, our method first forecasts the intermediate initial flow based on historically predicted flows. To effectively store multiple history flows, we design a memory bank with fixed length T, which stores the nearest T frames' predicted flows (F.41k-1- less than T, the first several frames' flows are directly stored in the memory bank as memory initialization. The memory bank is maintained by sliding window mechanism: When a new flow predicted, it is stored in memory bank, and the temporally-fartie guarantee efficiency, our stored flows are at the 1/16 resolution of the original image size.
When the number of predicted flows
one is discarded. To
Auto-regressive Next-Frame Forecasting, For the intermediate optical flow estimation, we predict the next-frame flow based previously stored flows as:
Feat (1) 1-1 - Trans (LFr+t
f1,41 = & ((Feat (1)
where (!) a transformer encoder for temporal modeling on all input frames, <(-) is a lightweight Conv2d projection, and fa.+1 denotes the initial flow predicted from the last token
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(1)
Feat.-1- Since this initial flow predicted from multiple history flows possesses the long-ränge temporal cues, it can provide an enhanced flow initialization.
3.3 AUTO-REGRESSIVE MULTI-STRIDE FLOW REFINEMENT
After getting the initial flow ft,411, we further leverage GRU-based iterations based on current obser- vations and multi-strid history weighting refine ft,++1 Notably, both refinements are conducted at the 1/16 of original resolution for fast inference.
GRU-based Iterative Refinement. We first leverage GRU-based refinement incorporate the information derived from current input images I, J Specifically, the feature maps E,, E of current image pairs are first extracted by ResNet16 (He al. 2016). Then, we obtain the interme- diate context feature from the current images as:
C, h ContextNettwork(( Te, I41). 1 Flow (lowHccd(h"),,
where ContextNetwork follows the setting in (Bargatin et al., 2025). c and ho respectively indicate the context feature and the initial hidden state in GRU. f is the predicted intermediate flow. If there are no stored history flows (the first image pairs), we use f as the input of GRU. Otherwise, we use the temporally forecasted flow fe,+1 as the input flow in GRU
when = otherwise,
f.t1 = (1.441
(3)
where fl+t1 is the input initial flow of the GRU module. Also, we associate two frames to obtain a correlation feature map V(u,v) v) =< (G(u),E+11f >, where < " > de- notes the dot product. u, denote the w-th and e-th element respectively in E, and E1+1- For the k-th iteration, the correlation values are retrieved by the look-up operation from V based on updated flow as: yk = LookUp(V, fh-1)) where f the intermediate out- put flow from the (k-1)-th iteration. Then, the intermediate motion feature M is generated by M MotionEacoddr((LPP(C MLP(())) Finally, the hidden state is iteratively up- dated by combining context feature c, motion feature Mr, and the previous hidden state h* GRU(c, A - Afterwards, the intermediate residual flows are obtained from the updated hid- den state Af FlowHeedd(h**, The output flow after the k-th iteration generated by per- point adding between the intermediate residual flow and initial flow as:
f.,+1 P/:z1 Aft.
We repeat the above process for K times to generate the final refined flow fi,++1 after GRU.
Multi-stride Temporal Weighting Refinement. The above GRU-based iterations only incorporate the context and motion features from the current timestamp, without consideration of multi-stride history flows or motions. This leads to poor adaptation to various motion variations. To address the problem, we continually refine 1K4-1 with multi-stride temporal forecasting.
Because the low-frequency and high-frequency information displays with unpredictable patterns along the entire video length, the single-stride temporal modeling in Section 3.2 is not effec- tive enough to capture long-range motion variations. To enlarge the temporal receptive field and strengthen the adaptation ability to diverse motion variations, we also generate forecasted flows by the multi-stride temporal modeling in a cascaded manner. Specifically, we further sample frames with strides of and 4 as:
(Feat) (2) (he(t-1,t-,..) Trans ( ((Feat!)) (TFeatt"))eftt-..ee (ee(t- f = o( (Feat
(5)
(Feat) (1) )i((e-11-B,..)) (d) (Feat liett-
f = of (Feat,
(6)
(Feat Juse )1e Trans (/) (eat Feat (2) Feat Sine = (Feat
(4)
Juse
where (2ans()) and (4) perform temporal modeling with stride 2 and stride 4, and Trans (
aggregates multi-stride features. Both f and fec+11 are used auxiliary supervision in the
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Table 1: Benchmark results on MPI-Sintel and KITTI-15. We report endpoint-error (EPE) on
Sintel (Butler et al. 2012) and Fl on KITTI-15 (Geiger et al. 2013).
Sintel Clean
Sintel Final
KITT1-15
Method
Reference
Mat.
Unm.
All
Mat.
Unm.
All
All
Non-Occ J
HowNer22 [g al. 2017
CVPR 17
1.56
25,40
4.16
2.75
30.11
5.74
10.41
6.94
PWC-Net Sun al 2018
CVPR' 18
1.45
23.47
3.86
2.44
27.08
5.04
9.60
6.12
RAFT Teed & Deng 2020
ECCV"20
0.62
9.65
1.61
1.41
14.68
2.86
5.10
3.07
GMFlow [Xu al 2222
CVPR 22
0.65
10.56
1.74
1.32
15.80
2.90
9.32
3.80
HowFormer [Htiang et a..
2022
CVPR 22
0.42
7.16
0,96
11.30
2.09
4.68
2.69
GMFlow+ XX air 202.50
TPAMT'23
0.34
1.10
2.37
4.49
Pairwise
Flowformer++ (Shi
20/23b
CVPR 0.39
6.64
0.88
10.63
1.94
4.52
AnyFlow (Jung cl (126)
CVPR'23
0.42
7.68
1.21
1.12
13.37
2.44
4.41
2.69
CroCoHlow (Wenzuepicl ct
2023
ICCV'23
0.39
6.85
1.09
1.21
12.42
2.44
3.64
2.40
DDVM (Saxena
(207.5)
NeurlPS'23 23
0.83
9.26
1.75
1.28
12.20
2.48
3.26
2.24
Flow Diffuser [Lii et all 2024
CVPR'24
0.38
6.23
1.02
0.97
10.67
2.03
4.17
2.82
SEA-RAFTILT wang ct al 2024b
ECCV"24
0.44
8.40
1.31
1.20
14.06
2.60
4.30
SAMFlow (Zhou el al.. 2024
AAAT'24
0.38
5.97
1.00
1.04
10.60
2.08
4.49
DPFlow (Mormiss el at. 2025
CVPR'25
0.39
3.56
2.12
CEDFlow++ 7Z00
2025
IICV'25
1.37
2.40
4.78
WAFT (Wang Deng
Arxiv'25
1.09
2.34
3.42
2.04
MFCFlow (Chen
20225
WACV"23
0.63
8.34
1.49
1.33
12.81
2.38
3.00
Molti-frame
TransFlow [u
2025
CVPR'23
0.36
6.77
1.06
0.99
10.96
2.08
4.32
SplatFlow Wang et ol. 2024a
UCC'22
0,51
6.06
1.12
1.06
10.29
2.07
4.61
2.96
StreamFlow (Sun el al. 2024
NeurIPS'24
0.38
6.42
1.04
0.82
10.44
1.87
4.24
2.45
MemFllo [Dong & Fu 2024
CVPR'24
0.43
6.09
1.05
0.93
9.93
1.91
4.10
2.56
MEMFOF Bargati of al., 2025
ICCV'25
0.40
0.96
0.88
10.30
1.91
2.94
1.97
ARFlow (Ours)
0.39
5.64
0.81
9.79
1.78
2.85
loss function to stabilize training and guide the learning of Trans (2) and Trans From the final
token of (f) we additionally predict learnable weighting parameter Wr,++1 dy (Feat ) Finally, we re-weight the output from the GRU refinement by incorporating multi-stride forecasted flows by:
Fi = Wt w1,++1 + (1 Wt.t+11
(8)
The weighted optical flow estimation Fi.+1 is the final output, which is also leveraged to update the memory bank to forecast the next frame pairs. The final flow estimation Fi,+1 are convexly upsampled to the input resolution as in RAFT (Teed & Deng 2020).
3.4 LOSs FUNCTIONS
Following SEA-RAFT (Wagg al. 2024b), utilize a mixture-of-Laplace (MoL) loss and apply RAFT-style deep supervision across iterations. Given T frames and K refinement steps, the training loss is
L
K-k cl,k MoL
where Cl.k denotes the MoL loss for frame at refinement k. Following Bargatin et al. 2025 ) we set y 0.85 emphasize later refinements. Parameterization and mixture details provided in the supplementary. In addition to K refined flows fl,411, we also apply the same Mol. objective to all intermediate flows, including fi,+11 f St,t+ fuse. and Fi,411-
4 EXPERIMENTS
4.1 EXPERIMENTAL SETTINGS
Evaluation Datasets and Metrics. We evaluate on three standard optical-flow benchmarks: Spring Meehl et al.) 2023b) (modern high-resolution video), MPI-Sintel (Butler et al. 2012) (syn- thetic scenes with complex motion), and KITTI-2015 (Menze & Geiger 2015) (autonomous driv- ing). We report endpoint error (EPE), l-pixel enror rate (Ipx), Fl (KITTI-15), and WAUC as com- parison metrics. The detailed definitions are described in the appendix.

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Published as a conference paper at ICLR 2026
Reference Frame
MemFlow
StreamFlow
DPFlow
Ours
Figure 4: Qualitative comparison Sintel test set. Compared with previous methods, ARFlow can predict sharper motion boundaries as highlighted in the circle. Please zoom in for details.
Reference Frame
MemFlow
SplatFlow
DPFlow
Qualitative comparison on KITTI test set. Compared with previous methods, our ARFlow can distinguish occluded areas as highlighted in the circle. Please zoom in for details.
Figure 5:
Network Architectures. For image feature extraction, we use a ResNet-FPN backbone He et al. (2016) (ResNet-34, dim= 512) shared by the context network (cnet) and feature network (fnet). onet takes concatenated RGB frames to produce 1/16-resolution features and the initial hidden state, while fnet extracts per-frame features for subsequent feature matching. On top of these coders, we adopt a standard RAFT/GMA-style backbone (Jiang et al.) (221)) with a 4-stage cor- relation pyramid (radius=4), GMAUpdateBllok (num_ blocks= 2, iters 6 at 1/16 resolution), and a learned convex upsampler for GRU-based Iterative Refinement. For the Temporal Trans- former, we first encode the optical flow and its uncertainty each time step using 2D convolutions, transforming the input (B, T. 6, H, W) into features (B, C. C, H, W). We then rearrange them as: (B, T, C, H, W) - (BHW, T, C"), and apply standard multi- self-attention along the temporal dimension. Moreover, we adopt multi-scale hierarchical Temporal Transformers (downsampled by the strides=1, 2, 4 over time) and fuse these sequences with another Temporal Transformer in the Auto-regressive Multi-Stride Flow Refinement module.
Pretraining and Fine- Fine-Tuning for Benchmark Submissions. We first pretrain on TartanAir (Wang et al. 2020) for 225k steps with crop size of 480 x 960, batch size of 64, and learning rate of 1.4 x 10 which requires about 13.1 GB of GPU memory. This model is then used to initialize training on FlyingThings3D (Mayer et al., 2016) for 120k steps with a larger crop size of 864 x 1920, batch size 32, and learning rate 7 x 10 (18.5 GB). Next, we extend the training to the combined T+ (FlyingThings (Mayer et al. 2016), Sintel (train) (Butler et al.) (012), KITTI- 15 (train) Geiger et al. 2013), and HDIK (Kondermann et al. 2016)) set for 225k steps under the same settings.
For benchmark-speciffc adaptation, we perform lightweight fine-tuning. On Sintel, we train 12.5k steps with 872 1920 crops, batch size and learning rate 10 requiring 23.8 GB of memory. On KITTI-15, we fine-tune 2.5k steps at 750 x 1920, batch size 32, and learning rate 3 x 10-3 requiring 20.8 GB of memory. On Spring (Mehl et 2023b), we run 60k steps with full-resolution 1080 x 1920 crops, batch size 8, and learning rate 4.8 x 10-5, requiring 29.4 GB of memory. We adopt MEMFOF (Bargatin et al. 2025) as the network architecture. By default, the temporal length of the memory bank is T 6, and the GRU refinement performs K = 6 iterations. The detailed network implementation is provided in the supplementary code.
Settings for Zero-Shot Evaluation. For zero-shot evaluation, following MemFlow (Dong & Fu 2024), we first pre-train the networks in the 2-frame setting on FlyingChairs (60k iterations) and Flying Things3D (150k iterations). We then enable temporal modeling and continue training on Flying Things3D for additional 120k iterations.
4.2 BENCHMARK RESULTS
Benchmark Results on Sintel and KITTI-15. Table T reports results on the two widely-used benchmarks. ARFlow ranks first on the official KITTI-15 benchmark (Geiger et al.) 2013), achiev- ing the lowest errors on both All pixels (2.85 vs. 2.94 MEMFOF and 4. of MemFlow) and Non-Occ pixels (1.91 vs. 1.97 of MEMFOF and 2.56 of MemFlow), On Sintel (Butler et al. 2012). ARFlow achieves state-of-the-an performance on the Final pass (1.78 vs. 1.87 of Stream and 1.91 of MEMFOF) and ties the best on Clean (0.96). These results highlight the advantage of our reto-regressive framework: (AFI) provides reliable initialization values from historical esti- mates, while (AMFR) integrates both short- and long-term temporal cues, enabling more accurate

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Table 2: Benchmark results on Spring. Runtime and maximum GPU memory usage were evalu- ated using an NVIDIA RTX 3090 GPU. Best results are respectively highlighted as first second OOM indicates out of memory. 'ndicates scene flow methods.
Inference Cost (1080p)
Spring (test)
Method
#Frames
Memory, GB
Runtime,
1px
EPE FI WAUC1
FI (uu
al. 2021
1.34
405
MeFlow Xu ét al.
2023a
2
1.32
1028
PWC-Net Sun et al 2018
2
1,41
76
82,265
2.288
4.889
45.670
FlowNet2 [gg of al. 2017
2
4.16
167
6.710
1.040
2.823
90.907
RAFT TTed Deng.
2020
2
7.97
557
6.790
1.476
3.198
90.920
RAFT3D [Teed & Deng
2021
2
13.962
2.528
6.889
81.267
GMA (Jiang al.
2021
2
13.26
1185
7.074
0.914
3.079
90.722
NO FINE. EE-TUNE
GMFlow Xu al.
2022
2
151
10.355
0.945
2.952
82.337
FlowFormer (Huang al.
2022
2
OOM
6,510
0.723
2.384
91.679
RPKNet Mormiisu
2024
2
8.49
295
4.809
0.657
1.756
92.638
Win-Wir Win Leroy al. 2024
2
5.371
0.475
1.621
92.720
MS- MS-RAFI+ (ahedi et al. [0244
5.724
0.643
2.189
92.888
MatchAttention Yan al.
2025
14.34
755
4.584
0,453
1.505
93.389
M-Fuse (F) MMeni ét al. 2025a
20.374
8.791
76.550
VideoHlow-BOFi (Shi
2023a
3
17.74
1648
VideoFlow-MOF (Shi cf al
20233
5
OOM
MemFlow (Dong & Fu. 2024
3
8.08
885
5.759
0.627
2.114
92,253
StreamFlow [Sun cl al. 2024
4
18.97
929
5,215
0.606
1.856
93.253
MEMFOF (Brrgatin et al. 2025
3
2.09
472
3.600
0.432
1.353
94.481
ARFlow (Ours)
8
2.10
403
3,587
0.428
1.313
94.501
CrocoFlow Weinzacpfel
2023
2.01
6524
4.565
0.498
1.508
93.660
SEA-RAFT (S) [Wang cl al.
20245
8.15
205
3,904
0.377
1.389
94.182
FINE-TUNT
SEA-RAFT (M) Waag cl al.
20224b
8.19
286
3,686
1.347
94.534
MemFlow (Dong & Fu. 2024
3
8.08
885
4,482
0,471
1.416
93.855
StreamFlow (Sun et al. 2024
4
18.97
929
4.152
0.467
1.424
94.404
DPFlow Mormmisu et al.
2025
2
10.39
990
3,442
0.340
1,280
94.663
MEMFOF (Bargatin ct al.
2025
3
2.09
472
3.289
0.355
1.238
18.186
ARFlow (Ours)
8
2.10
403
3.265
0.353
1.212
95.283
MemFlow
StreamFlow
Ours
Reference Frame
(
Error
Error color code
Figure 6: Qualitative comparison Spring test set. Compared with previous multi-frame methods MemFlow (Dong & Fu 2204)), StreamFlow (Sun et al. 2024), our ARFlow has more accurate flow estimations and lower errors as highlighted in the circle. Please zoom in for details.
and robust predictions. We also visualize the qualitative flow predictions as in Fig. and Fig. respectively. Our ARFlow performs better in terms of sharp boundaries and occluded areas.
Benchmark Results on Spring. Table shows the results on Spring. With full-resolution training (1080p), ARFlow achieves the state-of-the-art performance across all four metries (Ipx, EPE, Fl, WAUC), while being highly efficient both memory (2.1 GB) and runtime. This efficiency stems from avoiding redundant feature extraction and progressively forecasting frame by frame, making ARFlow the only method that combines SOTA accuracy with scalability to long sequences. We also visualize the predicted flow and its corresponding error maps in Fig. 6
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Published as a conference paper at ICLR 2026
Table 3: Zero-shot Generalization. ARFlow achieves the best cross-dataset generalization on KITTI-15 (train).
Table Compatibility evaluation on various base- lines. Consistent with (Wang et al. [2024b] [un et al. 2022; Morimitsu et al. 2025), all models are trained the combined Clean+Final (C+T) split and eval- uated on the Sintel and KITTI-2015 training sets for fair comparison. "PG." indicates the performance
Method
Sintel
KITTI- 15
Clean
Final
PWC-NNe
Sun
218
2.55
3,93
10:40
33.7
gain over the baseline.
Neow
el al
2017
2,02
3.14
10.10
30.4
RAFT
Tend & Thems
X00
1.4
2,71
5.04
174
Sintel (tmin))
KITTI-201S (miin)
SKF
Sun et
1.22
2.45
4.27
Method
GMFlooNNt
Zhoo et al
2022
Clean J Final |
L
PG.
FlowFormee
Houng
2023
SEA-RAFT(
Wang
2024h)
4.11
SCA-RAFTI.
Winz
2024b
1.19
3.62
12.9
ARFlow-S
1.12
3/22
9.5%
3,32
11.4
11.6%
AnyPlow
2123
1.10
2,52
3.76
12.4
DFFlow Morimisu 2025
1.02
2.26
3.37
11.1
FlowDiffase
Tnneral
2014
0.56
2.19
3.61
118
ARFlow-D
0.97
2.14
5.3%
3.08
10.3
7.2%
DPFlow
Monmitu et m|
1.02
2.26
11.1
(Pogai 2025
FloSSok
Poosi
WAFT
Wasn
ARFlow-F
5.0%
3,05
MEMFOF
Bargain
2025
1.20
3.91
2.93
WAFT Wang & Deng 2025
1.00
2.15
3.10
10.3
ARFlow (Ours)
0.58
2,07
2,86
9.2
ARFlow-W
0.91
2,01
6.5%
2.93
9.4
8.7%
4.3
ZERO-SHOO GENERALIZATION
Following common practice from Teed & Deng (020); Huang ct al. (2022): Dong & Fu (0024), we pretrain model the FlyingChairs (Dosovitskiy et al. 2015) and Flying Things3D (ayer al. 2016) datasets, and then directly assess its performance on the training splits of Sintel (Butler et al., 2012 and KITTI-15 (Geiger al., 2013). Table summarizes cross-dataset generalization perfor- mance. ARFlow achieves strong transfer performance across Sintel (train) and KITTI-15 (train), consistently outperforming recent baselines. These results demonstrate that our auto-regressive de- sign not only improves benchmark accuracy but also yields robust generalization to unseen datasets.
4.4 COMPATIBILITY EVALUATION ON OTHER BASELINES
As shown in Table 4 ARFlow consistently improves different basclines across Sintel and KITTI- 2015. For example, it yields 9.5% and 11.6% gains when integrated with SEA-RAFT, and also achieves 5-10% relative improvements when combined with the SOTA methods DPFlow, FlowSeek, and WAFT. These results indicate that our auto-regressive initialization and multi-striae refinement provide complementary temporal cues, serving as general plug-in that enhances both transformer- and pyramid-based architectures.
4.5 ABLATION STUDY
Effectiveness of AFI and AMFR. Table 5 (#1) evaluates the influence of the proposed com- ponents. Removing the Auto-regressive Flow Initialization (AFI) increases errors notably, e.g., Sintel (Clean/Final) from 0.88/2.07 to 0.95/2.15, and KITTI-2015 EPE rises from 2.86 3.05. Similarly, discarding the Auto- regressive Multi-stride Flow Re- finement (AMFR) degrades perfor- mance (Sintel 0.91/2.13, KITTI- 2015 EPE 3.00). These results con- firm that both AFI and AMFR are indispensable for accurate and ro- bust estimation.
Table 5: Ablation study. Settings used as default are under- lined. All models are trained on C+T for fair comparison.
Method
Sintel (train)
KITTI 2015 (trii))
Clean
Final]
EPE]
Fl-alll
#1: ARFlow (AF) approuches AF-wlo AFI
0.95
2.15
3.05
10.11
AF-wlo AMFR
0.91
2.13
3.00
9.73
#2: AMER Strategy
Single Stride Single Stride
0.89
2.89
9.52
Stride Design in AMFR. As shown in Table (#2), using only
0.90
2.12
2.93
9.55
Single Stride
0.91
2.12
3.00
9.68
Stride 1 + 4
0.88
2.07
2,86
9.21
single stride (I, 2, or 4) yields Combin-
#3: Temporal Length
weaker performance. ing multi-stride settings achieves the lowest errors (Sintel 0.88/2.07, KITTI-2015 EPE 2.86) compared
0.91
2.11
2.96
9,43
0.90
2,09
2.92
9.41
2.86
0.88
2.08
2.88
9.19
0.89
2.06
2.56
9.17

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Published as a conference paper at ICLR 2026
to any of the single stride settings, demonstrating the benefit of capturing both short- and long term motions.
Temporal Length. Table 5 (#3) investigates the impact of varying the memory length T. We observe consistent gains when extending from to frames, with the best results at 'T' 6 (KITTI- 2015 EPE 2.86, Sintel 0.88/2.07). Further increasing frames shows marginal change, suggesting trade-off between accuracy and efficiency.
4.6 DISCUSSION
Attention Weights in Temporal Modeling Transformer. We also visualize the attention weights in our designed auto-regressive transformer module in Fig. Z We observe that our transformer mainly focuses on dynamic objects and motion boundaries, which can provide an accurate motion prior for the next-frame flow forecasting, This ensures stable and robust flow initialization, resulting in excellent performance of our ARFlow.
time
Comporrison of Long term Future Prediction
LMPS (MemFlow)
(Duns (NfeFlow)
0.40
Aam-age
0.35
0.30
(A)
0.20
002
0.15
1.0
1.5
2.0
2.5
3.5
4.0
5.0
Time Step
BB)
Figure 7: Visualization of attention weights in our temporal modeling transformer.
Figure 8: Quantitative comparison of long-term future prediction by optical flow.
Downstream Task: Video Prediction. We evaluate ARFlow against MemFlow under different prediction horizons using LPIPS (lower better) (Zhang et al., 2018) and SSIM (higher is bet- ter) (Wang et al., 2004). As in Fig. X when the prediction horizon increases, MemFlow exhibits rapid degradation in performance, while ARFlow demonstrates a much slower decline, highlighting its stronger temporal modeling and autoregressive prediction capability. Unlike MemFlow, which requires an additional prediction head, ARFlow naturally predicts the next-frame optical flow, lead- ing to more stable and consistent performance over longer horizons. Additional implementation details are provided in the supplementary material.
5 CONCLUSION
In this paper, we propose novel multi-frame optical flow estimation pipeline based on auto- regressive transformer models. We formulate the task with the progressive flow estimation frame by frame and introduce an auto-regressive next-frame forecasting strategy to provide accurate and ro- bust initial flow. Then, refinement module combining both current motion features and multi-stride history forecasting flows is leveraged to generate fine-grained flow estimates and refine the initial predictions. Extensive experiments on standard benchmarks demonstrate that our method achieves state-of-the tort---rrtt-tt accuracy while maintaining competitive efficiency.
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ACKNOWLEDGEMENTS
This work was supported by National Key R&D Program of China (Grant No. No.2024YFBB4088000) and EU
XTREME (Grant No. 101136006). It was also supported in part by the Natural Science Foundation of China under Grant 02225309,U244222022 62361166632.
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APPENDIX
A OVERVIEW
The supplementary materials are structured as follows:
We give more detailed illustrations about the metrics, loss functions, settings in video pre-
diction, and clip-wise training strategy in Section B
More experimental results are provided in Section
Qualitative results on KITTI, Waymo, and NuScenes are presented in Section D
Screenshots for benchmark results on KITTI, Sintel, and Spring are shown in Section E
Section discloses the limited and strictly assistive usage of a large language model (LLM) during manuscript polishing.
video demo of both synthetic and real-world driving scenes is appended to the supple- mentary materials, and the detailed network implementation is also provided in the supple- mentary code.
B IMPLEMENTATION DETAILS
B.1 DEFINITION OF METRICS
We provide more formal definitions of metrics used in the main text.
EPE is the average per-pixel Euclidean distance between the predicted flow and the reference flow over all valid pixels.
The Ipx rate is the fraction of valid pixels whose flow exceeds pixel.
'The Fl score (KITTI-2015) is the percentage of pixels whose error is larger than three pixels and also greater than five percent of the ground-truth magnitude.
-AUC summarizes performance by integrating the inlier rate over error thresholds from zero to five pixels, with larger weights near zero; a precise definition appears in the supplementary material. For optical flow, the weighted area under the inlier-rate curve (WAUC), introduced with VIPER (Richter
al. 2017). aggregates the inlier percentage over error thresholds.
Let f(r) E [0, 100] denote the percentage of pixels whose endpoint error is at most pixels. With weight that emphasizes small thresholds, the metric is
2 / WAUC
f(r) 5 5-7 dr,
(10)
5 Ju
yielding a score between 0 (worst) and 100 (best).
B.2 MIXTURE-OF-LAPLACE LOss
Following SEA-RAFT, we use a two-component Laplace mixture for each scalar flow coordinate. Given target y and predicted mean μa, mixture weight a (0,1), and log-scale coordinate negative log-likelihood is
R, the per-
Emixlap (y; cr, 3,,1)) (1) = log lv- +
1-a
exp
(11)
2e
For single optical-flow prediction, the image-level loss sums over spatial locations and the two flow coordinates:
LMOL
(d) (uistap(Whh. an, St Ph
(d)
(12)
2HW
h=1
(z.9)
All parameters (o. (4) predicted per pixel.
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B.3 SETTINGS FOR VIDEO PREDICTION.
For a fair comparison, we follow MemFlow (Dong & Fu y given the last frame 1,, we first predict the forward flow /1-1+11 and monocular depth map using DPT (Ranftl al. 2021). We then render a candidate next frame by forward-warping I with Softmax Splatting (Niklaus & Liu 2020). which yields splatted image and a disocclusion mask identifying unsupported pixels. Finally, we complete those regions via ZITS inpainting (Dong et al. 2022), producing the synthesized frame I
B.4 CLIP-WISE TRAINING STRATEGY.
Compared to prior mainstream methods, the biggest difference is that our ARFlow keeps the orig- inal temporal sequence during the training process. However, most of the previous multi-frame methods, such as MemFlow Dong & Fu (2024) and StreamFlow [Sun et al. (2024), interrupt the original temporal sequence by the random training batch shuffle. To be specific, previous methods like MemFlow use the standard batch-wise training strategy, where timestamps among consecutive input batches are continuous. Thus, their temporal modeling remains constrained these seg- ments, like 3 frames within one batch, rather than truly covering arbitrary-Iength sequences because of the shuffled timestamps. This train-test inconsistency cannot reasonably be regarded as effec- tive temporal modeling for long-sequens videos. Furthermore, if the temporal range in one batch increased, they would suffer from infeasible computational burdens as Figure 2 the main manuscript.
In contrast, we treat the whole sequential video clip as input and use a clip-wise training method inspired by MOTR ZZng et al. (2022), rather than the batch-based one. In this case, our memory and supervision can model the temporal information for the whole sequence by sliding along all the frames. Therefore, temporal receptive field can be viewed as the whole-sequence awareness for each video.
C ADDITIONAL EXPERIMENTS
We also supplement more ablation studies and prediction results here.
Temporal Flow Memory Resolution. We ablate the resolution at which cached flows are stored, comparing 1x, 1/4x, and 1/16x. All models are trained on C+T for fair comparison. As shown in Table storing flows at 1/16x maintains accuracy Sintel and KITTI while substantially reducing memory. We therefore adopt 1/16x as the default setting in all experiments.
we vary the number of GRU refinement iterations K E (4, 5, 6, 7,8} while keeping training on C+T for fairness. As reported in Table 6 (#2), increasing K from 4 to consistently improves Sintel and KITTI metrics. However, increasing to K-7 or K yields mixed yet marginal changes (some metrics slightly improve while others slightly degrade), with overall gains being negligible relative K-6 and accompanied by higher computation and memory costs. For a balance between speed and accuracy, we choose to perform 6 iterative refinements. For reference, MEMFOF reports using K= in their final configuration; since our pipeline builds on MEMFOF as the baseline but benefits from stronger autoregressive initialization and multi-strid forecast fusion, fewer refinement steps suffice in practice.
Number of Iterative Refinements (K). Following MEMFOF Bargatin et al. 2025
Pre-trained Temporal Transformer. We replace our Temporal Transformer from our original net- work with two recent pre-trained Transformer backbones: WAN2.2 (5B) Wan et al.] (2025) and Longcat-video (14B) Team et al. (2025). As reported Table 6 (#3), adding recent pre-trained transformers cannot bring additional performance gains, and designed temporal transformer al- ready has effective temporal modeling ability.
Sub-sequence Results on Spring Dataset. All the 11 sub-class 1px values on Spring benchmark are listed in Table 7 where our ARFlow surpasses all recent -fate-of-the--at methods on most metrics, which demonstrates the excellent performance of our proposed method.
Optical Flow Prediction. We also evaluate the performance in flow prediction in Table g Com- pared to previous methods, ARFlow also achieves the best accuracy in the flow prediction task,
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Reference Frame
MemFlow
Fiom hout
Faw
Fan Aost
DPFlow
Ours
Figure 9: Qualitative results of MemFlow, DPFlow, and our method on the KITTI benchmark with occlusions. Sourced from official leaderboard submissions.
outperforming the recent method MemFlow-P by 10.2%, 13.9%, and 17.8%, respectively, on three datasets. We attribute this great prediction performance our auto-regressive paradigm, which nat- urally captures long-range temporal cues and facilitates the flow extrapolation in frame-by-franne auto-regression manner.
Table Additional Ablation study. Settings used as default are underlined. All models are trained on C+T for fair comparison.
Method
Sintel (train)
KITTI-2015 (train)
Clean.]
Final].
EPR]
F1-alL,
Temporal Flow Memory Resolution
0.90
2.06
2.87
9.27
1/4
0.89
2.09
2,85
9.31
1/16
0.88
2.07
2.86
9.21
#2:
The number of iterative refinements (K).
0.90
2.11
2.98
9.89
0.88
2.10
2.87
9.35
0.88
2.07
2.86
9.21
0.88
2.08
2.87
9.20
0.89
2.09
2.85
9.23
#3: Pre-trained Transformer WAN2.2 (5B) Wan et al. 2025
0.90
2.08
2.88
9.56
Longcat-video (1-B)] Teain et al. 2025
0.91
2.07
2,89
9.43
w/o pre-trained Transfomerrs
0.88
2.07
2.86
9.21
D QUALITATIVE RESULTS ON KITTI, WAYMO AND NUSCENES
We provide additional qualitative comparisons on KITTI, Waymo, and NuScenes datasets.
Visualizations on Occlusions. On KITTI test sequences, our ARFlow produces sharper motion boundaries and better handles occluded regions compared with recent multi-frame methods, as high- lighted in Fig. 9 Competing approaches often exhibit artifacts such as blurry edges incorrect motion in challenging occlusion areas, whereas our autoregressive initialization and multi-stride refinement enable more accurate predictions.
Influence Different Temporal Lengths. We prove the effectiveness of larger temporal recep-
tive fields by comparing estimation differences occlusions in Fig. 10 Shortening the temporal modeling length (T = 4) leads to much worse estimation accuracy on the occluded signal.
More Visualizations Spring. We supplement another visualization sample in Fig, From the figure, our estimated flows the dynamic eyes are more accurate compared to prior methods like MemFlow and StreamFlow.
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Table Sub-sequence Benchmark results on Spring. The 1px metrics for 11 categories are listed.
Best results are respectively highlighted as first second
1px in Each Category
Method
low-det.
high-det.
matched
unmat.
rigid
non-rig.
not sky
sky
s10-40
PWC-Net
Sun et
2018
82.268
81.747
82.069
90.400
82,817
78.090
81.575
92.761
81.402
82.189
89.693
FINE-TUNE TUNE NO FINE-TUNE
Flow/er22
[Tg 6l
2017
6.346
5.691
48
3
29.404
6.039
16.908
1.862
5.816
49.693
RAFT
Teed x Demp
2024
6.426
64.087
5.999
39.481
4.107
27.088
5.250
30.188
3.134
5.301
40.
GMA
Daang or 2021
6.699
66.203
6.281
39,892
4.276
28.247
5.614
29.263
3.645
5,389
40.327
GMFlow Au
9,935
76.613
9.060
63,949
6,800
37.258
8.952
31 686
5.412
9.901
52.944
FlowFornkr [HoR 2ir
2022
6.144
64.219
5.766
57.294
3,527
29.084
5,500
21 21.858
3.381
5,530
35,344
MomFlow Dong Fu 2024
5.394
63.348
5.107
32.755
3.293
24.422
4.494
24.990
2.918
4.820
32.071
StrearFFlow (San al [0203
4.869
59.550
4.559
32.343
2.865
22.987
4.435
17.059
2.597
4,492
29.067
MEMFOF Bargatm cl
2045
3.254
58.072
3.049
26.384
1.510
19.416
3.708
1.961
1.315
4.574
20.081
ARFlow (Ours)
3,180
$7.251
2,973
26.375
1.675
17.526
3.395
5,505
1.084
4.244
22.009
CrocoFlow (Weinzeepfel
2023
4.209
60.594
3.848
34,200
2.194
22.501
4.479
5.868
1.225
4.332
33.134
(EA-RAFT(S7 Wane et a.
20246
3,536
61.951
3.172
34,228
1.662
20.871
3.974
2,855
1.264
4,871
23.378
SEA-RAFT (M) [Wing
3.323
60.986
3.025
31,058
1.561
19.769
3.757
2.616
1.241
4.760
21.237
McmFlow Dong & FU 2024
4.119
61.703
3.742
35.115
2.391
20.306
3.934
12.809
1.305
4.437
31.184
StreamFHlpw (Sun
3.424
34.304
1.986
20.544
3.986
6.678
4.381
DPFlow (Morinnis cr 2025
3.102
56.941
2,859
27.563
1.500
18.132
3.522
2.218
1.188
3.998
20.786
MEMPOF [Bargali
2.751
25.551
1.446
17.236
3.327
2.723
4,202
19.270
ARFlow (Ours)
2,926
56.655
2.717
25.955
1.436
17.108
3.325
2.362
1.079
4.160
19.133
Table 8: End-point-error of flow prediction on Flying Things3D (Final)
Mayer et al.
2016)),
Sintel (Final), and KITTI-15.
Method
FlyingThings33 (Final)
Sintel (Final)
KITTI-15
Warped Oracle
14.76
5.76
MemlFlow Dong Fu 2024
15.70
12.95
OFNet Cimmana 2024
13.76
6.03
12.43
7.56
5.38
8.82
Ours
6.79
4.63
7.25
Zero-shot Generalization on Waymo and Nuscenes. Moreover, we visualize zero-shot general- ization on Waymo (Sun et al., (2200) and NuScenes (Caesar et al., 2020). Importantly, these datasets are not used during training, yet our ARFlow still achieves high-quality optical flow predictions. As shown in Fig. [2] Fig. 13 Fig. 14 and Fig. [5] our method maintains robust performance in diverse driving scenarios, confirming its strong generalization ability beyond the training distributions.
E SCREENSHOTS FOR BENCHMARK RESULTS ON KITTI, SINTEL, AND SPRING
We include official benchmark screenshots for KITTI-15, Spring, and Sintel in Fig. 16 Fig. 17 and retrieved on September 23, 2025 (KITTI-15 and Spring) and September 25, 2025 (Sintel).
Fig. 18
On KITTI-15 (Geiger et al. 2013), ARFlow ranks first among all optical flow methods, achieving the best results both All and Non-Occ pixels. Note that methods ranked above ours the leaderboard mainly rely on scene flow or additional 3D information rather than pure optical flow.
On the Spring benchmark (Mehl et al. 2023b), ARFlow ranks first among all open-source meth- ods, surpassing strong baselines such as MEMFOF and DPFlow.
For Sintel (Butler al. 2012). our method achieves second place among open-source methods, matching or outperforming recent state-of-the-art approaches.
These results consistently highlight the advantages of our autoregressive paradigm: scalable tempo- ral modeling, reliable initialization, and robust refinement across datasets.
F LLM USAGE STATEMENT
A large language model (ChatGPT) was used in a strictly limited assistive manner during manuscript preparation. Its usage confined to: (i) spelling and grammatical error checking; (ii) minor phras- ing and wording refinement to improve fluency without altering technical meaning, methodology, analyses, conclusions; and (iii) occasional condensation of repetitive sentences and suggestions
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Reference Frame
Ours (7=4)
Ours (7=6)
Figure 10: Qualitative results of different temporal lengths on the KITTI benchmark. Sourced from official leaderboard submissions.
MemFlow
StreamPlow
Ours
Reference
(+) Flow
(b) Error
Error color code
Figure 11: Qualitative results the Spring benchmark.
for consistent formatting. The LLM did not contribute to research ideation, problem formulation, method design, experimental execution, data processing, result analysis, drafting of technical con- tent, or formulation of conclusions. The LLM is not an author and bears no responsibility for the content.
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Reference Frame
Flow Result
Figure 12: Qualitative results of our method on the Waymo dataset (Sun et al. 2020 (1). Note that our model was not trained on the Waymo dataset.
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Published as a conference paper at ICLR 2026
Figure 13: Qualitative results of our method on the Waymo dataset (Sun et al. 2020 (2). Note that
our model was not trained on the Waymo dataset.
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Published as a conference paper at ICLR 2026
Reference Frame
Flow Result
Figure 14: Qualitative results of our method on the NuScenes dataset (Caesar et al. 2020 (1). Note
that our model was not trained on the NuScenes dataset.
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Published as a conference paper at ICLR 2026
Figure 15: Qualitative results of our method on the NuScenes dataset (Caesar et al. 2020 (2). Note that model not trained on the NuScenes dataset.
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Published as a conference paper at ICLR 2026
The KITTI Vision Benchmark Suite
TOTOTA
KIT Kaflsruhe Institute of Technology
J
FRGICAAL AL BECCEAI MOUT
project Karlaruhe Institute Technology and Teyota Technological
Chicago
home
setup
stereo
flow
sceneflow
depth
odometry
object
tracking
road
semantics
raw data
submit results
A. Gelger
P. Lenz
C. Stiller
R. Urtasun
Log In
Optical Flow Evaluation 2015
The stereo 2015 flow 2015 scene flow 2015 benchmark consists of 200 training scenes and 200 test scenes (4 color Images per scene, saved less png format). Compared to the stereo 2012 flow 2012 benchmarks, comprises dynamic scenes for which the ground truth has been established semi-automatic process. Our evaluation server computes the percentage bad pixels averaged 200 test images. For this benchmark, we consider pixel to be correctly estimated if the disparity or flow end paint error is <3px or <5% (for scene flow this criterion needs to be futfilledd for both disparity maps and the flow map), We require that all methods use the same parameter set for all test pairs. Our development kit provides details about the data format as well as MATLAB C++ utility functions for reading and writing disparity maps and flow fields. More details can found Object Scene
all ground truth pixels all
Autonomous Vehicles (CVPR 20151.
Evalustion ground truth All pizels
Evalustion area All pizels
Nethod
Setting
Fbail
Density
Runtime
Environment
Compare
SCa:Flowe1 Nomber
1.98
s
(Python)
US
sode
2.22
5.99%
X
100.00
(Python)
Schwmit a. Jowil, N. Sean a know ySRurTop as Srie orttee For Decurmet roed- Sreen Paw 1887 inventtooll Confernnce image Prooeecing (CPP 2005.
68FMc
2.48 x
4.59 %
2.65 X
100.00
0.35 s
GPU 2.5 GPS (Python)
8.27
6.02.
2.09 8
100
0.1
2.5 Grz (Python)
Wong Zhang J. V. lu,
D. S0teriion
Sontial Dine
Solcric Inseraaional Jeamnll Computer
SEA, Fexd:sast
2.08
6.95%
2.89 X
100.00 %
0.07 s
1 core @ 2.5 Gnz (Python)
88
2.07
7.11%
2.01 %
100
0.5
1 cure @ 2.5 Grz (Python)
HEHFOE
code
2.60
4.50
2.94%
100.00 x
0.45
GPU @ 2.5 Ghz (Python)
CaMLIRAFT
sode
2.08
7.37%
X
100.00
2.5 (Python C/Cc-)
LM T U. Ma. andi. Worg Leamtn Ortical 3ox and Score Flecwtth 3kl actiool Coneen Races TRAMI 2003.
CamLiFio
code
2,313
7.04%
3.10%
100.00
1.2
GPU io 2.5 Ghe (Python C/C+.)
Orikca
5.05
0.
100.00
Sawena, C. Hommmer J. Has Kac Horeed, San and
Fiet The Surtrri Ettoct edhess z1 Ditiittooo Model byr Dicck fine and Soccolar de Esiretion Neuris 2003.
11
WW6FT-2-0822
2.98.8
4.96%
3.31 x
100.00 %
0.24
NVIDI RTX3050
TDFlow
3.00
5.06
3.34%
100.00
(Python)
(C/C++)
CaMLIRAFTNN
2.76 5
100.00
(Python C/C..)
Lia 3 iu. A.. Us andi. Weng: Loamen Drize Sceare Scame Flocosth Sedtectionn Conere bares, enty prop www.iri 13817 2523.
15
BaFlow
2.75
6.36
3.43
100.00
0.53 s
core d 2.5 Ghz (C/C++)
16
W-FUSE
sade
2.66
7.47%
3.46
100.00
1.3
CPU
Figure 16: Screenshots from the KITTI-I5 optical flow benchmark on the official website, retrieved on September 23, 2025. Note: The methods ranked above ours are based on scene flow rather than optical flow.
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Published as a conference paper at ICLR 2026
SPRING Dataset & Benchmark
Lofte
All mnteni Inforets
LSR
BLI
SFT
TUR
230
CND
1MI
CELT
SON
CDI
130
DXC
SCMRT
SIR
Figure 17: Screenshots from the Spring optical flow benchmark the official website, retrieved September 23, 2025. Note: To the best of our knowledge (as of September 23, 2025), WAFTv2 does not have publicly available paper.
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Published as a conference paper at ICLR 2026
Results and Rankings
Result for methods appear here after upload them and approve them for public display.
EPE all
EPE
EPE
d0-10
d10-60
d60-
80-10
s10-40
840+
GroundTruth (1)
Vinumiine Resurtts
ViCo VideoFlow MOF
1.618
0.768
8.543
1.966
0.601
0.348
0.392
1.198
8.705
Visseline Hentrits
0.788
2.087
0.334
0.402
8.793
VideoFlow-MOF
1.649
0.788
8.660
2.080
0.609
0.334
0.403
1.243
8.804
Visualize Ressuits
8
1.652
0.794
8.645
1.881
0.887
0.445
0.391
1.090
9.284
MemoFlow
1.892
0.805
8.917
2.125
0.631
0.338
0.407
1.262
9.098
Vesuelire Plesuitt
VideoFlow-BOF
1.713
0.812
9.054
2.056
0.636
0.387
0.387
1.242
9.422
ARFlow
1.786
2.102
0.380
0.312
Visualize Plesuitt
adex001 XI]
1.824
0.908
9.270
2.446
0.682
0.421
0.416
1.424
9.746
Visualtze
MemFloowT [1]]
Visuaoline Plesuits
StreamFlow
1.874
0.824
10.435
2.091
0.635
0.350
0.409
1.240
10.674
Visuellow Henorits
(12)
0.961
0.696
0.329
11.501
MEMFOF-XL
1.890
0.863
10.257
2.092
0.634
0.496
0.319
1.181
11.513
Visualize Risuitts
MEMFOF-L (14)
1.907
0.877
10.302
2.101
0.637
0.512
0.324
1.128
11.844
MemFlow
1.914
0.931
9.928
2.332
0.738
0.419
0.430
1.382
10.556
Visueeliee Plesuitt
SemFlow-2vieee [1])
1.925
0.902
10.265
2.376
0.768
0.359
0.445
1.396
10.622
Viscellon
MEMFOF
2.121
Visualizee Pienuits
Figure 18: Screenshots from the Sintel optical flow benchmark on the official website, retrieved on September 25, 2025, Note: To the best of our knowledge (as September 25, 2025), only VideoFlow has publicly available paper; the other higher-ranked methods do not have publicly available publications.
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Reviews:
[{"review_id": "Ttku0QDJ7Y", "reviewer": "Reviewer_tPdM", "summary": "It presents ARFlow, an autoregressive multi-frame optical flow estimation method designed to address key limitations of existing approaches—limited temporal receptive fields, high computational/memory overhead, and insufficient multi-stride temporal modeling. By introducing an Auto-regressive Flow Initialization (AFI) module and an Auto-regressive Multi-stride Flow Refinement (AMFR) module, it achieves good performance and efficiency.", "questions": "Please refer to the weaknesses above.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Innovative Autoregressive Paradigm: The autoregressive design naturally captures long-range temporal dependencies and supports arbitrary video lengths, which is a good advantage for real-world applications.\n2. Efficient Module Design: The AFI module and AMFR effectively modeling both short-term subtle motions and long-term occlusions and achieves good efficiency. \n3. Exceptional Performance and Efficiency Balance: According to the paper, ARFlow delivers leading results across three standard benchmarks, outperforming recent strong baselines (e.g., MEMFOF, StreamFlow) while maintaining constant memory usage.", "weaknesses": "1. The fonts in Figure 4 and Figure 5 are not uniform.\n\n2. The speed of StreamFlow seems to be inaccurate. If the SIM pipe is not enabled, it will cause many redundant frame calculations and a lot of time waste, please the author check this point again.\n\n3. Considering that this is a brand new structure, it is recommended that the author include more detailed base network structures in the final version and open source them. I am also wondering whether the author has verified that if replacing some pre-trained Transformer methods will bring improvements?\n\n4. More indicators and methods on Spring can be reported.", "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": "zdZ9cqHHUW", "reviewer": "Reviewer_okv5", "summary": "The paper presents ARFlow, a novel approach to multi-frame optical flow estimation. The proposed method includes two key components: the Auto-regressive Flow Initialization (AFI) and the Auto-regressive Multi-stride Flow Refinement (AMFR), both of which significantly enhance the flow prediction accuracy and computational efficiency. Extensive experiments on various benchmark datasets (KITTI-2015, MPI-Sintel, Spring) demonstrate that ARFlow achieves state-of-the-art performance while maintaining a constant memory usage of 2.1 GB, making it highly efficient.", "questions": "MemFlow typically maintains a memory length of 3 and can also be applied for Arbitrary-length videos. And I cannot tell any other difference with previous methods in terms of videos length from the paper. As for figure 2, I think there may be some minor implementations problems for MemFlow and leads to memory usage increased (, better engineering can solve this problem). \nOverall, this is a nice work with solid experiments, I would like to give a rating of accept.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "Innovative Approach:The paper introduces a unique solution to multi-frame optical flow estimation by utilizing an auto-regressive model. \nState-of-the-Art Performance: ARFlow achieves top-tier results on standard optical flow benchmarks, including KITTI-2015, MPI-Sintel, and Spring.", "weaknesses": "1.  The paper does not provide the detailed architecture of its Temporal Transformer. How this transformer process the flow input? Does it employ spatial-temporal attention directly?\n2. What’s the inference time per-frame and # model parameter when compared to previous method such as MemFlow?\n3. I am also curious about the accuracy of the predicted flow with Temporal Transformer (stride 1). How it performs under the flow prediction setting of MemFlow-P?\n4. For the title, I find the phrase of ‘Arbitrary-length videos’ seems to be some wise over-claimed or confused. As previous methods like", "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": "darDP6yOX1", "reviewer": "Reviewer_v3yg", "summary": "This work proposes a novel multi-frame optical flow estimation network in a so-called \"Auto-Regressive\" manner, which features flexible and efficient processing of an arbitrary number of input frames and achieves certain accuracy improvements compared to previous methods. This Auto-Regressive approach is not reflected in the internal architecture of the network but rather in the overall pipeline. Nevertheless, it represents a relatively innovative approach in the methodology of multi-frame optical flow estimation.", "questions": "Please refer to the weaknesses. If necessary, I will update the score based on the response and further discussions.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "1. This work proposes a novel approach for multi-frame optical flow processing.  \n2. The method can handle videos of arbitrary length with significantly reduced memory usage and optimized speed.  \n3. While improving efficiency, the work enhances model performance through effective temporal modeling techniques.", "weaknesses": "1. The term \"Auto-Regressive\" is somewhat nuanced here. Unlike traditional methods like LLMs that perform next-token prediction in discrete spaces, this approach implements autoregression through modifications to the pipeline. Therefore, comparing it with LLMs in related work is less meaningful. It would be more relevant to discuss autoregressive video generation works, as they also incorporate autoregressive changes in their pipelines.\n\n2. Table 1: The font size is too small.\n\n3. Table 2: Should include more Spring metrics. In the Non-finetune section, additional methods like SEA-RAFT could be added.\n\n4. Recommend including more metric evaluations on the Sintel test set.\n\n5. Are there other cases for Figure 6? The hair strand difference in the bottom-left corner is not clearly visible.", "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": "NJBDbTyZo9", "reviewer": "Reviewer_hJDw", "summary": "The paper proposes ARFlow, a novel framework for multi-frame optical flow estimation, which leverages an auto-regressive paradigm to predict optical flow in arbitrary-length videos with marginal computational overhead. ARFlow adopts a progressive next-frame forecasting strategy, with two key components: the Auto-regressive Flow Initialization (AFI) module, which predicts the next-frame initial flow using multiple history flow estimates from the memory bank, and the Auto-regressive Multi-stride Flow Refinement (AMFR) module, which iteratively refines the flow using multi-stride temporal information. The experiment results show that ARFlow achieves state-of-the-art performance on benchmarks like KITTI-2015, Sintel, and Spring and handles long sequences efficiently with consistent memory usage.", "questions": "- The paper treats optical flow estimation as a streaming problem, which aligns with the inherent properties of the auto-regressive paradigm. However, in most cases, a complete video sequence is already available, so it's natural to anticipate information from later frames. Actually, many existing methods do this. I'm curious why the paper uses unidirectional auto-regression, sacrificing bidirectional information that may further improve accuracy.\n- How does ARFlow handle large motions and sudden occlusions? The auto-regressive paradigm relies on historical information, but how does ARFlow handle corner cases when historical information becomes invalid?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper proposes using auto-regressive prediction for optical flow estimation, which is novel. By progressively forecasting the next-frame flow based on historical estimates, ARFlow can handle long sequences more efficiently, significantly improving the scalability of optical flow estimation.\n- The paper presents good benchmark results, with ARFlow achieving state-of-the-art performance on popular datasets like KITTI-2015, Sintel, and Spring.\n- The paper promises to open-source the code after publication, which is a positive development for the open-source community. ARFlow can also serve as a baseline method for subsequent improvements and comparisons.", "weaknesses": "- In Line 047, the paper points out that existing multi-frame methods fail to exploit sufficient temporal cues because of Limited temporal receptive fields. However, the temporal intervals used in this paper are similar to those of mainstream methods in both the AFI (T=6) and AMFR (max stride=4) modules. Furthermore, I would question the validity of long-distance information for optical flow estimation tasks. The experiments in this paper also indicate that the benefits diminish over excessively long distances.\n- Although the introduction of auto-regressive estimation has reduced the computational overhead of ARFlow, it still falls short of real-time applications.", "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 (Poster)