Streaming Drag-Oriented Interactive Video Manipulation: Drag Anything, Anytime!
Authors: Junbao Zhou
Yuan Zhou
Kesen Zhao
Qingshan Xu
Beier Zhu
Richang Hong
Hanwang Zhang
Venue: ICLR 2026 Poster (2026)
Keywords: Streaming Video Manipulation
Drag-Style Manipulation
Primary area: generative models

Abstract: Achieving streaming, fine-grained control over the outputs of autoregressive video diffusion models remains challenging, making it difficult to ensure that they consistently align with user expectations. To bridge this gap, we propose \textbf{stReaming drag-oriEnted interactiVe vidEo manipuLation (REVEL)}, a new task that enables users to modify generated videos \emph{anytime} on \emph{anything} via fine-grained, interactive drag. Beyond DragVideo and SG-I2V, REVEL unifies drag-style video manipulation as editing and animating video frames with both supporting user-specified translation, deformation, and rotation effects, making drag operations versatile. In resolving REVEL, we observe: \emph{i}) drag-induced perturbations accumulate in latent space, causing severe latent distribution drift that halts the drag process; \emph{ii}) streaming drag is easily disturbed by context frames, thereby yielding visually unnatural outcomes. We thus propose a training-free approach, \textbf{DragStream}, comprising: \emph{i}) an adaptive distribution self-rectification strategy that leverages neighboring frames' statistics to effectively constrain the drift of latent embeddings; \emph{ii}) a spatial-frequency selective optimization mechanism, allowing the model to fully exploit contextual information while mitigating its interference via selectively propagating visual cues along generation. Our method can be seamlessly integrated into existing autoregressive video diffusion models, and extensive experiments firmly demonstrate the effectiveness of our DragStream.

Published as a conference paper at ICLR 2026
STREAMING DRAG-ORIENTED INTERACTIVE VIDEO MANIPULATION: DRAG ANYTHING, ANYTIME!
Junbao Zhou', Yuan Zhou'r, Kesen Zhao', Qingshan Xu', Beier Zhu', Richang Hong",
Hanwang Zhang'
Nanyynn Technological University
Hefei University of Technology
[yuan. zheu, qingshan. xu, beier.2hr zhang))@n.n [JUNHAC001, kiseN002)ee.ntu.eddu... hongro.hfut.@gmail
Code: Cotpf//trss///ahha.ccc////aaa//a
page:
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[Animation: Trans]
(Animttonn: Defor]
Andration: Trans]
Anisstion:
Rot
Editing:: 3D Rot
Figure 1: Examples of our REVEL task. The streaming video manipulation results shown above including both Editing and Animat ion with drag effects such as object translation ("Trans")) deformation ("Defor"), and rotation ("Rot") produced by our DragStream method.
ABSTRACT
Achieving streaming, fine-grained control over the outputs of autoregressive video diffusion models remains challenging, making it difficult ensure that they con- sistently align with user expectations. To bridge this gap. propose stReaming drag-oriEnted interactiVe vidEo manipuLation (REVEL), new task that en- ables users to modify generated vidcos anytime anything via fine- fine-grained, in- teractive drag. Beyond DragVideo and SG-I2V, REVEL unifies drag-style video manipulation editing and animating video frames with both supporting user- specified translation, deformation, and rotation effects, making drag operations versatile. In resolving REVEL, observe: i) drag-induced perturbations cumulate in latent space, causing severe latent distribution drift that halts the drag process; iī) streaming drag is easily disturbed by context frames, thereby yielding visually unnatural outcomes. We thus propose training free approach, DragStream. comprising: an adaptive distribution selffrectiiicationn strategy that leverages neighboring frames' statistics to effectively constrain the drift of latent embeddings; iī)
selective optimization mechanism, al- lowing the model to fully exploit contextual information while mitigating its inter- ference via selectively propagating visual cues along generation. Our method can be seamlessly integrated into existing autoregressive video diffusion models, and extensive experiments firmly demonstrate the effectiveness of our DragStream.
"Yuan Zhou (yua @ @ntu.edu.s)) the corresponding author of the paper.

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Published as a conference paper at ICLR 2026
INTRODUCTION
Video Diffusion Models (VDMs) have shown impressive capabilities in generating photorealistic videos, and their success inspired a broad range of generative applications, including image anima-
tion Lei et al. (2025); Hu (2024), text-based video editing Ceylan et al. (2023); Liu et al. (2024). camera-controlled video generation Zheng et al. (2024); He et al. (2024); Bai et al. (2025), etc. With
the recent remarkable progress in autoregressive VDMs Yin al. (2025) Huang et al. 2025 and
diffusion acceleration Zhu et al. (2025b); Wang et al. (2025), researchers have been focusing more on achieving controllable video generation in a streaming manner, thereby enabling users to interact with VDMs and alter synthetic videos on the fly. For instance, Kodaira et al. (2023 2025), ) Lin et al.
2025) proposed directly finetuning VDMs to support streaming video generation conditioned on text, camera viewpoint, and human pose, whereas Liang et al. (2024) realized training- text- guided streaming video translation by introducing a booking-back strategy.
Drag-style operations have become crucial control signal for VDMs due to their fine-graines nature and user-friendly interactivity Zhou et al. (2025); Wu et al. (2024); Deng et al. (2024): Wang et al. (024);; Namekata et al. (2024); Zhao et al. (025). However, it remains challenging to realize streaming, fine-grained control over the outputs of VDMs through drag-style operations. To mitigate this dilemma, propose new task, stReaming drag-oriEnted interactiVe vidEo manipul REVEL aims to allow users to modify generated videos at any
(REVEL). As shown in Figure | time and on any content via fine-grained, interactive drag, making generated videos consistently meet users' requirements. We go beyond prior methods, such as Drag Video Deng et al. (2024) and SG-I2V Namekata al. (2024). by unifying drag- oriented video manipulation as editing and animating video frames, with both supporting user-specified translation, deformation, and rotation effects. thereby making drag operations versatile and establishing a standard paradigm for drag-style video manipulation.
Given the fine-graing finegrained nature and high diversity of drag-based video manipulation, solving REVEL is non-trivial. Directly finetuning VDMs to realize REVEL usually incurs expensive training costs requiring training VDMs on large-scale, fine-grained drag-style data by hundreds or even thousands of H100 GPU hours Yin et al. (oods) et al. (2025): [uang et (202)) mmaking it impracti- cal for resource-conss constrained scenarios. This observation naturally leads us to ask a key question: How can high-quality REVEL be achieved without incurring prohibitive computational costs?
We propose solving the above question from a training-frve free perspective in this paper, so as to ef- fectively reduce training expenses. However, observe that there exist two key challenges: i) perturbations induced by drag operations easily accumulate in latent space, thereby causing severe latent distribution drift that totally halts the drag process; ii) streaming drag is easily disturbed by context frames, resulting in visually unnatural content. Therefore, we propose a new DragStream approach. Specifically, we first design an Adaptive Distribution Rettffeeciii (ADSR) strategy that suppresses the distribution drift of latent code by considering statistics from neighboring frames, Selective
thereby effectively overcoming drag interruption. We also introduce Optimization (SFSO) mechanism, which propagates visual cues from preceding video frames se- lectively in both spatial and frequency domains. As a result, we can fully exploit the information of context frames while relieving their interference. ADSR and SFSO enable our DragStream to achieve high-quality results on REVEL without incurring prohibitive training costs, while allowing it to be seamlessly integrated into existing autoregressive VDMs. Extensive experiments provided in Section 3 and the appendix consistently demonstrate the superiority of our proposed approach.
Here, summarize the main contributions of this paper:
We propose stReaming drag-oriEnted interactiVe vidEo manipuLation (REVEL), new task that enables users to drag anything anytime during video generation, thus achiev- ing streaming, fine-grained control over the outputs of VDMs via drag-style operations.
We identify two key challenges in solving REVEL within a training-free paradigm: i) drag- induced perturbations cause severe latent distribution drift and halt the drag process; and ii) streaming drag is disturbed by context frames, resulting in visually unnatural outcomes.
We propose DragStream, which incorporates Opatial-Frequencyy Selective Optimization (SFSO) mechanism and an Adaptive Distribution Self- Rettifectiintioon (ADSR) strategy to effectively suppress context interference and mitigate distribution drift in latent code.
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Extensive experiments clearly demonstrate the effectiveness of our approach in addressing REVEL, showing that it achieves high-quality streaming drag-style manipulation, remains training-free, and offers plug-and-pln integration with existing autoregressive VDMs.
2 RELATED WORK
Streaming Video Generation. StreamDiffusion Kodaira et al. (2023). SVDiff Chen et al. 20024) and StreamDiT Kodaira al. (2025) are recent representative streaming text-guided video genera- tion models, in which VDMs are either trained from scratch or finetuned to enable streaming control via text prompts. Lin et al. (0225) proposed an autoregressive adversarial post-training strategy that enables VDMs to operate as one-step autoregressive generators, supporting conditions hu- man pose, camera viewpoint, and text. Liang et al. (2024) designed a text-based streaming video translation model by preserving historical information across video frames using feature bank.
Drag-Based Video Generation and Editing. Wu al. 2024); Wang et al. (2024) proposed finetun- ing bidirectional VDMs with trajectory conditions, thereby realizing trajectory-guided video gener- ation. Zhang et al. (2025a) proposed unifying text, image, and trajectory conditions into a DiT framework Peebles & Xie (2023), while Geng et al. (2025); Zhang al. (2025b) further trained VDMs on dense trajectories. Namekata et al. (2024); QQu et a (2024); Jain et al. (2024); Deng al. (2024) resorted to training- free frameworks. Deng et al. (2024) introduced a drag-based latent optimization strategy to realize drag-oriented video editing. Namekata et al. (2024) further consid-
ered semantically aligned features /hhu al. (2025a) 2024; 2025b) during dragging, whereas Qiu et al. (2024) achieved trajectory- -uidedd video generation by imposing guidance on both attention and noise construction.
REMARK 1. i) Despite the progress in streaming video generation, current models rarely sup- port highly flexible, fine-grained drag-style operations a streaming manner-a key challenge our work aims to address. ii) Existing drag-based video generation and editing methods are not tailored for streaming tasks, making them unsuitable for achieving fine- tine-graied,, streaming control over the outputs of autoregressive VDMs. iii) Directly finetuning VDMs for realizing streaming drag-styla manipulation is computationally expensive, usually requiring training VDMs on large-scale drag- style data by hundreds or even thousands of H100 GPU hours, which is unacceptable for resource- constrained scenarios. Different from finetuning- based methods, our DragStream is training -rree and can be seamlessly integrated into existing autoregressive VDMs. iv) Beyond previous works, we unify drag-style video manipulation as editing and animating video frames with both supporting user-specified translation, deformation, and rotation effects, thus making drag operations versatile.
STREAMING DRAG-ORIENTED INTERACTIVE VIDEO MANIPULATION
We first give the definition of our stReaming drag-oriEnted interactiVe vidEo manipuLation (REVEL) task in Definition |
For the summary of the main notations, please refer to Section B
Definition 1 (REVEL) Let I& denote the k-th video frame produced by autoregressive VDMs. REVEL aims to enable users to utilize drag-style operations UR to modify video frames for Yk: Z+ and ensures that subsequently nearby frames are consistent to kk so as to realize streaming, fine- grained control over outputs of VDMs and make generated videos always meet users' requirements.
We argue that there exist major limitation in current brag-based video manipulation, namely the lack of unified definition of drag-style manipulation operations. Existing drag- video edit- ing methods focus on dragging objects in generated videos, with the goal of yielding the effects of translation, deformation, and rotation [eng al. (2024); also, these methods are generally unable to allow users to animate video frames via dragging. By contrast, trajectory-guided video gener- ation models are designed to generate video clips by moving objects along trajectories, with their motion rendered by VDMs; however, they are not flexible enough to specifically allow users to determine the type of drag operations, e.g., deforming object shape, translating objects, or rotating them around a center point Namekata et al. (2024); Zhang et al. (2025a). Since both of these settings are incomplete, we propose unifying drag-style video manipulation operations in Proposition
Proposition 1 (Unifying Drag-Style Video Manipulation Operations) We unify drag-style video manipulation as enabling users to perform editing and animation on video frames via drag-style op- erations, with both supporting user-specified translation, deformation, and 2D/3D rotation effects.
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Veriater
Masinrars of
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[Animation: Trans]
Distribution drug process
Challenge 1: Latent distribution drift
iAnimat
SFS0: Context interference artifacts
Animatiion: Rot
O
Challenge 2: Context interference
Figure Examples Challenge I and Challenge 2
Here, editing refers directly modifying the content of generated video frames, whereas animation represents generating a video clip from an existing frame accorling to user-given drag instructions.
REMARK 2. Here, we clarify how our REVEL task differs from prior works on drag-based video editing and generation. Drag Video Deng et al. (024 is recent typical drag- based video editing approach. Different from our REVEL,, it only supports drag-based editing and does not allow users to animate video frames. Moreover, Drag Video does not support the 2D object rotation operation.
SG-12V Namekata et al. (2024) and Tora Zhang et al. (2025a) are two typical trajectory-guided video generation approaches. Both of them focus solely on animating images by moving objects along trajectories with VDM-rendered motion, without allowing users to flexibly achieve more fine- grained drag-styl effects, such as editing object shape rotating objects around center point by specific angle. Also DragNeXt ZZou et al. (2025) does not support streaming-styy editing. Most importantly, these methods are incapable of achievinbg drag-oriented video editing and animation in streaming manner.
We propose addressing REVEL from a training-free perspective, and identify that there exist two key challenges, summarized in Challenge and Challenge respectively.
Challenge 1 (Latent Distribution Drift) Perturbations induced by drag-style operations easily ac- cumulate in the latent space of autoregressive VDMs, which leads severe distribution drift of latent code and thus interrupts the drag process.
We show Challenge Figure (a). The figure shows that the mean and variance of latent embed- dings change significantly once drag operations applied, while the maximum and minimum val- ues exhibit obvious fluctuations. This instability drives the latent embeddings ("w/o ADSR+drag") drift away from the original distribution ("Ref: w/o Drag"), thereby disrupting the drag process. We find that latent distribution drift may cause undesirable change of object attributes, such as color and category, shown in the second row of Figure 2 (a). The use of our ADSR strategy ("w/ ADSR+Drag") can effectively suppress the distribution drift. We will introduce it in Section 4.2.2
Challenge 2 (Context Interference) Streaming drag is easily disturbed by context frames, mis- leading VDMs to produce visually unnatural content and thus substantially degrading video quality.
We show Challenge 2
in Figure 2 (b). The results in Figure 2 (b) clearly indicate that visual cues from previous frames may mislead the subsequent generation severely, e.g.. the features around the handle points spuriously guide the model produce duplicated ears the rabbit and artifacts
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F-lh Etrratioo
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Figure Schematic illustration of our DragStream, where an Adaptive Distribution Self- Rectification (ADSR) strategy and Spatial-Frequency Selective Optimization (SFSO) mechanism are designed to suppress latent distribution drift and context interference, respectively.
the ("wlo SFSO"), which obviously lowers the quality of generated videos. We will introduce how to overcome context interference by using our SFSO strategy in Section 4.2.3
METHODOLOGY
4.1 PRELIMINARIES
Autoregressive Video Diffusion Models. Autoregressive VDMs refer to hybrid generative frame- work that integrates diffusion models with chain-rule decomposition, i.e., PT[ II PT (T)))= where P(T (T)})=)-11) is modeled by iteratively denoising a Gaussian noise latent code z E (0, I) to clean latent code zú conditioned on proceeding frames }T
The KV caching strategy Zhou et al.) ( [Huang et al.) (2025): Yin al., (2025) often employed during inference to further reduce computations, thereby accelerating autoregressive generation.
repre- sent drag operations for 1 where E [ Ji=mo indicates set of user-specified handle regions that require to be dragged, and C = {nr , ) represents the corresponding drag instruc- tions. The indictor r/* = Edit iiciting or Animat ion determines whether the video frame r is to be edited animated, whereas ç indicates the type of each drag operation. For animation, O (h, {p. >= l:kmm c. if ç Rotation; otherwise, O (h. {p. > 1:k-mmm ). Here, hf represents handle point, {p. b represents m discrete target points sampled along a drag trajectory, assigned to subsequent m video frames, and ct denotes rotation center of the han- dle region H. For editing, O {h! p. c c Rotat ion; otherwise, O {h p Here, each drag operation considers only one target point, since the editing task ignores intermediate drag states. Also, binary mask M is utilized specify the non-editable region of the frame P
Drag-Style Operation Formats. Following Zhou al. 2025). we use U = (E* C*
4.2 DRAGSTREAM: DRAG ANYTHING, ANYTIME IN TRAINING-FREE PARADIGM
4.2.1 OVERALL PIPELINE
We first introduce the overall pipeline of our DragStream. Suppose that users observe the video frame & during streaming generation and intend to manipulate I by giving the instructions t * Ck.. where E -HH indicates handle regions, and C {1) c )
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denotes the corresponding drag instructions. We use I to represent video frame produced during dragging, where k = l 1y Edit Editing; otherwise, k' > since new frames are animated during
Animation.
We take the handle region H as an example to illustrate our method. As exhibited in Figure wc first denoise श to 24. and extract the features F(z?i) by concatenating features from the multiple layers the DiT denoiser (-)( K', V')==0x''1), where ( (K', Vi are the cached keys and values of context frames. We then estimate the position of the handle region H after being dragged within the features F(z according to the user user-given drag instruction:
Y II - = G(k', H,."" O.
(H c == Lp c:p,), if = Rotatioo Trans H 0 p. -p!),
s.l., 9(K', " c/ 0
else.
Here, HH, c. 8) denotes rotating the handle region H, around the center point cf by an angle 0, and Tra,s(H 2 indicates translating H by an offset &. Y,K is a binary mask that indicates the target position of H in the extracted features F(z ), and II -Y. is the coordinate mapping from H. of = are also extracted and detached reference features, Fzer(= Moreover, we interventionally adjust the reference features according to the coordinate mapping, Fres [+; -+:- J. thereby perturbing the original latent code and transforming the handle
Finally, the latent code zf. is iteratively optimized. In each iteration, the features
F(zf.). ach ()
gion features the target position Y The latent code z// of the new frame r can be updated by reconstructing the features from the original handle region at the target position of F(zØ")
Qh Oz7
* # zt.
where
Cro: = [(()))) Y
Frer(ztt H x
111 [F(())))
M Finss Finse(2))))
M l12
Caue
Lox
(3) Here, Laoe denotes a reconstruction loss, and Lest represents a constraint term that ensures the consistency the non-editable region M of Fiai (= = .eff:..detcchhh () indicates the initial features of zty before conducting iterative latent region optimization. Our ADSR and SFSO strategies employed during the above iterative latent region optimization process overcome Challenge I and Challenge which are detailed in Section 4.2.2 and Section 4.2.3 respectively.
REMARK 2. If r7 Animation then k' > k. which represents cross-frame optimization paradigm, i.e., using the perturbed features Free ( [ to guide the denoising process of z/C' of the new frame r'K, 1f = Editing, K'' k. which can be seen as self-guided optimization, i.., using the detached features Frer (z III H of I to guide the re-denoising of z/.
4.2.2 ADAPTIVE DISTRIBUTION RECTIFICATIO
We propose a simple-yet-effective strategy, Adaptive Distribution Self-Rectification (ADSR), ad- dress the latent distribution drift issue caused by cumulative perturbations Challenge as pro- vided in Proposition] 2
Proposition 2 (Adaptive Distribution Rectifeeiiii)) Suppose users apply drag-style opera- tions to the frame Kk. The statistics μTT and oT of the preceding neighboring latent embeddings (श4 -L. of T& are recorded, where frT and oT are the mean and standard deviation.
We propose using fr and oT to rectify the distribution of 2 after each optimization iteration:
= Iter.opt im(z "t U - * OTT + pT.
(4)
01
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where Iter_optim()) denotes iteration of the latent optimization, and p and o' / denotes the mean and standard deviation of { -b, -L._L..-1//ff.. As exemplified in Figure (a), our ADSR can efffectivly suppress the distribution drift of latent embeddings, while significantly improving video quality and preventing undesired changes in object attributes during dragging. This aligns with the findings provided in Figure [7] showing that ADSR consistently improves model performance across the evaluation metrics ObjMC, DAI, FVD, and FID. For more details, please refer to Section S
4.2.3 SPATIAL-
SELECTIVE OPTIMIZATION
We design a Spatial-Frequency Selective Optimization (SFSO) mechanism to overcome Chal- lenge [ It fully exploits the information of context frames while relieving their interference via conducting information selection in both frequency and spatial domains during iterative latent re- gion optimization.
High-frequency information-though capturing finer visual tendss to mislead VDMs produce unnatural results, as it carries more noise perturbations Fan al. (2019); Li et al. (2020): by contrast, low-frequency details. We argue that it crucial harness the strengths of both high- and low-frequency informa- tion while alleviating their inherent limitations during the wrag-oriented optimization process. We therefore propose a Switchable Frequency- domain Selection (SFS) strategy Proposition 3
iittaatioonwiiee more robust- lacks sufficient fine graiied visual
Proposition 3 (Switchable frequency-doman Selection) Let (1.ii=1. represent the layers of the DiT denoiser that are used to construct reference features, and let X,, denote the input features of the layer 1, SFS is applied to the self-attentinn of the layer (= I:T. to build reference features with switchable frequency components in each iteration of the latent region optimization process:
K 1 V
Linear.projector/
(5)
K = Concat( - k.
Concat 1- V.C).
(6)
[Kt V. = IFFT (BtTeeBtte
)), = Random(w),.... WN))
(7)
X = sel self-attent on @t, K V.t))
(8)
Here, (K/ 1-ukk-1 and (V.) 1= denote cached keys and values, X denotes the extracted reference features of the layer 4, Butterw(. w) represents the Butterworth filter with the cutoff' frequency w randomly selected from [wi)= and (-) and IFFT(.) represent the 2D Fourier transform and 2D inverse Fourier transform.
By using SFS strategy, in each iteration, the information of different frequencies can be propagated to the latent embeddings 2 of r by the reconstruction loss Czec, thus fully exploiting information from context frames, while preventing high-frequengy information from dominating the drag process and inducing artifacts in generated frames.
In Proposition 4 we also design Criticality-driven Spatial-domain Selection (CSS) strategy to prevent over- optimizaiii of the background within editable region, which is beneficial for further reducing unnatural content.
Proposition 4 (Criticality-driven Spatial-domain Selection) We selectively back-propagate gra- dients in spatial domain, avoiding the drag process undesirably affecting the background:
G.K. ŌL
2111
(9)
Dzk.
where GK a Gaussian filtering map that decays w.r.t. the distance to the center point (xe, yo) of the edited region
GK [x, v] exp
(I 22 2a
(y
yc)
] s.l., 0,
W 2
and Bg
H 2
(10)
20
W and H are the width and height of the handle region's minimum bounding rectangle. and a is hyperparameter scaling the spread of the Gaussian and set as 1. The use of SFS and CSS can further improve video quality, which is demonstrated by experiments given in the main paper and appendix.
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Streaming generation
(Antration: Rot]
(antsation: Rot]
SG-12V
DilggVilen
[Editing: Bot]
[Anination: Trans]
Drag farluee
DragVuleo 5G-12V
ion
[Animstion: Trans]
[Animation: Trans]
Distortion
SG-12V
DragWideo
[Editing: Thans]
Drag failure
(Anisation: Trans]
Dragkkieen SG-12V
Drag failure
Artifact
Figure 4: Visualization results achieved by DragStream on REVEL. Note that Editing produces only one video frame, but we insert an extra subsequent frame to maintain layout consis- tency with Animatio inn.
EXPERIMENTS
Since REVEL is new task, no existing approaches have been specifically designed to tackle it. We adapt two training-fre free methods, CG-I2V et al. (2024) and Drag Video Deng et al. (2024), to the REVEL task for comparison. Please refer to Section of the appendix for details about our experimental setup, including implementation details, evaluation metrics, and compared baselines.
5.1 MAIN RESULTS
Visualization Results. The visualization results achieved by our method are shown in Figure Compared to SG-12V and Drag Video, our DragStream produces obviously more natural and higher- quality streaming drag-style video manipulation results. For instance, better preserves object ap- pearance and structure, while exhibiting fewer visual distortions, artifacts, and drag failures. These results validate the effectiveness our method in addressing the REVEL task. More visualization results achieved by DragStream provide in the appendix; for details, please refer Section
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PVD
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Figure 5: Quantitative performance achieved by our method in terms ObjMC, FVD, FID, and DAI. "4" indicates that lower values correspond to better performance.
ObJMC([)
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Ablation study on the key components our DragStream.
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Figure 7: Analysis the influence of the cutoff frequency "Switch" represents
frequencies are switchable during the latent region optimization.
Quantitative Performance. The quantitative results in Figure 2 demonstrate that our DragStream consistently outperforms SG-I2V and DragVideo scores indicate that our DragStream achieves higher video quality than SG-I2V and DragVideo. Video. On the other hand, achieving the best ObjMC and DAI scores demonstrates that our DragStream approach realizes more precise object dragging, aligned with the findings shown in Figure
Video again. On one hand, the lowest FID and FVD
5.2 ANALYSIS
Ablation Study. In Figure 6. we conduct ablation study to investigate the influence of each com- ponent. The results indicate the full method achieves the best performance. Discarding SESO ("w/ ADSR") leads to significant performance degradation, while further removing ADSR ("w/o ADSR, SFSO") results in an even greater decline. These results demonstrate the importance of the ADSR strategy and the SFSO mechanism. Similarly, using the full SFSO is better than using CSS SFS alone. We also analyze the influence the cutoff frequency in Figure 7 We can see that both small and large cutoff frequencies lead to performance drops. By contrast, our switchable frequency selection strategy achieves the best performance, as it fully exploits contextual information while mitigating tighffrequency interference by preventing them from dominating the drag process.
Runtime Analysis. Table [ exhibits the run- time analysis of our DragStream approach. Our SragStrrem is based on an iterative optimization scheme. In the table, we investigate the influ- ence of the iteration number I. We find that set- ting already achieves satisfactory perfor- mance, achieving 23.05 ObjMC and 0.051 DAI, while incurring only 0.13s additional runtime frame compared with the baseline without DragStream (i.e., I = 0). Decreasing the itera- tion number-sue such I = 2 or 3 ceptable drag-based manipulation performance, with ObjMC and DAI clearly outperforming those of the baseline (i.e., I = 0). All the experiments the table are conducted NVIDIA H20 GPU.
Table I: Runtime analysis of our DragStream approuch. In the table, "RF" denotes runtime per frame, and / indicates number of iterations drug oriented lalent optimization.
Experiments
RF
ObjMC (L)
DAI (L)
0
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1=
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27.67
0.054
=
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24.55
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7= (Ours)
0.30s
23.05
0.051
further improve execution speed, while still maintaining

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[Animation: Trans]
Face occluded by cup
face re re-energes
[Anination: Trans]
occluded by cup
re-energes
Figure Streaming drag with object occlusion and Re-emergence.
Generrated Led Videos
10s
[Animation: Trans]
18s
Aninated Videos
115
Videos
[Anination: Trans] Aninated
Figure 9: Streaming drag in long video scenarios.
[Animation:
Defor]
Unreasonable Overstretch
(Animation:
Trans]
Physically Implausible Drag
Figure 10: Failure cases under unreasonable and physically implausible conditions.
5.3 COMPLEX STREAMING MANIPULATION
Occlusion and Re-emergence. In Figure 8 we also study our DragStream in the scenario object occlusion and subsequent re-emergence. We find that our approach shows promising performance in this scenario and produce smooth video results. This is because VDMs are trained on massive amounts of data and thereby learns rich prior knowledge about object occlusion and scene transition.
Streaming Drag Long Video Generation. In Figure 9 we study the use of our DragStream for achieving streaming drag in long video generation. As shown in the figure, despite that accumulated errors remain a challenging issue for current autoregressive VDMs, our method can still effectively realize drag-based manipulation. For more results, please refer to Section Pf our appendix.
5.4 FAILURE CASES
We observe a failure case of our method. As shown in Figure I0 our method fails to realize high- quality manipulation under highly unreasonable and physically implausible conditions, such ma- nipulation instructions severely conflict with prior knowledge learned by VDMs large-scale data.
CONCLUSION
We propose stReaming drag-oriEnted interactiVe vidEo manipuLation (REVEL). a new task that aims to allow users to achieve streaming, drag-style control over the outputs of autoregressive VDMs. To solve REVEL, we propose a training-free approach, DragStream, which employs an Adaptive Distribution Self-Reetificatio Optimization (SFSO) mechanism. ADSR effectively constrains the drift of latent embeddings by leveraging neighboring frames' statisties, while SESO fully exploits contextual information while mitigating its interference via selectively propagating visual cues along generation in spatial and frequency domains. These two strategies enable method to achieve superior performance REVEL and allow seamless integration into existing autoregressive VDMs. We hope this work will inspire excellent solutions address the streaming drag-style video manipulation problem.
(ADSR) strategy and design Spatial-Frequency Selective
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ACKNOWLEDGMENTS
This research is supported by the National Research Foundation, Singapore under its Al Singapore Programme (AISG Award No: AISG3-RP-2022-030) and NRF-NRFF11--2024-0004
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CONTENTS
Introduction
2
Related Work
Streaming Drag-Oriented Interactive Video Manipulation
Methodology
Preliminaries
DragStream: Drag Anything, Anytime a Training- Free Paradigm
4.2.1
Overall Pipeline
4.2.2
Adaptive Distribution Self RelffRRetification
6
4.2.3
Selective Optimization
Experiments
5.1
Main Results
8
5.2
Analysis
5.3
Complex Streaming Manipulation
10
5.4
Failure Cases
10
6
Conclusion
10
Use of LLMS
16
Summary of Main Notations
16
Experimental Setup
16
Implementation Details
16
Evaluation Metrics
16
Compared Baselines
17
Streaming VS. Non-Streamin Drag-Style Video Manipulation
17
More Visualization Results
18
F
Visualized Analysis of Switchable Frequency
18
Visualized Analysis of Gaussian Filtering Map
20
Ablation Study on Objective Function Ltot
21
Compatibility DragStream
Drag-Style Operation Types
22
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K Why Need Context Frames?
23
Additional Video Results Supplementary Materials
23
M Robustness Analysis Hyperparameters
23
Results Controlling Bidirectional Models
24
0 Discussion of Prompt-Drag Conflicts
24
Additional Results on Long Video Generation
25
Long-Duration Drag-Oriented Manipulation
26
Object Leaving and Re-Entering
27
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A USE OF LLMS
LLMs were only used to provide minor writing assistance in preparing the manuscript, such as grammar polishing and readability improvement. No parts of the methodology. experimental design, analysis, or results were generated by LLMs. The all ideas, experiments, and conclusions are entirely completed and drawn by the authors of this paper.
B SUMMARY OF MAIN NOTATIONS
In Table we provide a summary for the main notations used in this paper.
Table 2: Summary of main notations.
Notions T
Descriptions
The k-th video frame generated by VDMs.
#1
The latent embeddings of I at the denoising timestep T. The user-specified drag-stylc operations for the frame r The set of user-specified handle regions for the frame r The corresponding drag instructions for the handle region E
Uhh (E" c+}
E h
C (n) Ct himto
H. Y
The binary mask that indicates the i-th user-specified handle region of the frame r The binary mask indicates the target position of H. that is be dragged in r*
II # -YK
The coordinate mapping from the handle region H. to the target position Y.K
"
The indictor determines whether frame I be edited or animated. The indictor determines the type of drag operations, i.e. translation, deformation, and rotation.
O
points sampled from the drag trajectory given for the handle region H
M
The user-specified non-editable region of the video frame I
G²
The Gaussian filtering map used for the frame I during latent region optimization.
Lp pt
angle at the center point et w.r.t. the trajectory points p and p.
1 P -Pi
The offset of the sampled trajectory point p. w.r.t. p".
pau pu
The mean/ /standard deviation of the latent embeddings zty.. Query/key /ke//value /value features about the frame r The object function used in the latent region optimization. The reconstruction loss used in the latent region optimization.
Q" ( .V
Lro:
Lawe
Lex
constraint term in latent region optimization. The denoiser of VDMs.
ca
F(-)
The function that extracts the features of latent code from eg (-).
C EXPERIMENTAL SETUP
C.1 IMPLEMENTATION DETAILS
We implement our DragStream in PyTorch and run it on an NVIDIA H20 GPU card. We choose Self-Forcing Huang et al. 20255) as our main base autoregressive VDM with the number of denoising timesteps T = 4. We follow SG-I2V to use the AdamW Loshchilov & Hutter (2017) optimizer during latent optimization, with the learning rate set as x 10 10- Following Deng et al. 2024 we perform latent region optimization at the denoising timestep T 3. where the features of latent code are extracted from the 12-12 15 layers of the DiT denoiser, the number of iterations is set to per trajectory point, and the set of cutoff frequencies is set (0.2, 0.4, 0.6, 1). Following Zhang et al. (2025a), we annotate 204 video clips generated by Self-Forcing with diverse drag trajectories and scenes, to serve new benchmark for evaluating model performance on our proposed REVEL task, i.c., realizing fine-grained, drag-style control over the outputs of video generation models.
C.2 EVALUATION METRICS We evaluate model performance the REVEL task using four metrics: Fréchet Video Distance (FVD) Unterthiner et al. (2018), Fréchet Inception Distance (FID) Heusel et al. (2017), DAI Zhang et al. (2024), and ObjMC Wu et al. 2024). Since FVD and FID well-defined metrics, omit
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the explanation for them; for more details, please refer to Unterthiner et al. (2018); Heusel et al. 2017). We provide the details of ObjMC and DAI below.
ObjMC. ObjMC Wu et al. 2024) is metric to evaluate the motion fidelity of the manipulated object in the video. is calculated as the average distance between the trajectory of the manipulated object in the generated video and the groundtruth trajectory specified by the user. To generate the trajectory of the manipulated object, we utilize Co-Tracker 3 3[Kareev et al.] (2024) to track the points scattered in the original region of the manipulated object, and then compute the average position of these points in each frame to form the trajectory. Lower ObjMC scores indicate that the manipulated
object in the generated video closely follows the user's specified trajectory, reflecting better motion fidelity.
DAL DAI Zhang et al. (2224) video manipulation. Specifically, DAI calculates the average difference between the latent features of the original handle region and the corresponding manipulated region in the final output. The
metric for evaluating the quality of drag editing in image and
metric is defined as:
N DAI
1 (2 .r)],,)]])))))) = r)]l (1+2r)²
N Σ
(11)
where N is the number of trajectory points, while =f and = the latent embeddings of the original frame I and the manipulated frame I respectively. r is the radius of the area we calculate DAI and (t(p,r) denotes a square area centered at point p with a side length of 2r + 1. pf and p are the i-th trajectory points in the original frame I and the manipulated frame I respectively. We set 20 following /hhou al. (2025), which is suitable for measuring the consistency inside the manipulated region. Lower DAI scores indicate that the manipulated region in the generated image or video closely matches the target region specified by the user, reflecting better drag editing quality.
C.3 COMPARED BASELINES
We emphasize that our proposed task, stReaming drag-oriEnted interactiVe vidEo manipuLa- tion (REVEL), is entirely new. Thus, to the best of our knowledge, no existing approaches have been specifically designed to address it. For comparison, we adapt two training-free approaches, SG-I2V Namekata et al. (024) and Drag Video Deng et al. (2024), to our REVEL setting. Like our DragStream, both SG-12V and Drag VragVideo// follow the latent optimization paradigm; however, they are not equipped with our ADSR and SFSO strategies, designed to address Challenge I and Challenge 2 in REVEL. We do not include the finetuning based methods such as Tora Zhang et al. (225a)) and Dryhhng Wu et al. (024)) in our comparisons. First, our DragStream is training- methods unfair. Second, Tora and DragAny-- thing are not specifically designed to solve our REVEL. Adapting them to enable autoregressive generation streaming control of VDMs would be non-trivial, as requires finetuning on large- scale, fine- eeningr drag-style data by hundreds or even thousands of H100 GPU hours Yin et al. (2025); Kodaira et al. (202)); Huang al. (2025), which stands in direct contrast the core philos- ophy of our work achieving high-quality REVEL without incurring prohibitive training costs. We leave exploring the REVEL task from a finetuning- based perspective for our future research.
free, making direct comparisons with finetuning-
D STREAMING VS. TON-STREAMING DRAG-STYLE VIDEO MANIPULATION
We provide a comparison between streaming and -treaminnn drag-style video manipulation in Figure A fundamental distinction lies in the different type of used VDMs. In streaming drag-style video manipulation, video frames are generated autoregressively; thus, when an unsatisfactory video frame is observed, users can directly feed drag-style operations to models and modify videos on the fly. In contrast, non-streaming drag-style video manipulation relies on conventional bidirectional VDMs that generate entire video clip at each time they are based modeling bidirectional information across frames, requiring users to regenerate the whole video clip when they find frame unsatisfactory. That is why adapting finetuning based, non-streaming drag-style video manipula- tion approaches to REVEL is non-trivial as we mentioned in Section (C.)) would require trans- forming the bidirectional generation paradigm totally into the autoregressive manner, which in
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the
frames
video CLSp
Autoregressive VDMs
Franc &
Drop Ferdback
Bidirectional VOMs
Once an
Plan
Hon unsutisfactorr fame is encoounteeed the whole video clip needs to be regeneratidd
Manipulate
Franc
observed,
dl
(+) Streaming drag-style video manipulation
(b) stres-steamnng drag-style manipulation
2
2013
2012
(c) Autoregresstve VDMs
Bidrrcttional VDMs
Figure 11: Comparison between streaming and non-streaming drag-style video manipulation.
turn necessitates collecting large-scale, fine-grained drag-style dataset and finetuning VDMs on it by hundreds or even thousands of H100 GPU hours Yin et al. (2025); Huang et al. (2025).
In addition the type of VDMs, another key difference lies in the manipulation process. In stream- ing drag-style video manipulation, if users find a video frame unsatisfactory and wish to edit it, they only need apply drag operations to that specific frame. In contrast, video manipulation, users must provide drag operations for the all subsequent frames to maintain cross-frame consistency, since information flow in bidirectional VDMs is bidirectional shown in Figure 6 (d), i.e., subsequent video frames can influence preceding video frames. Also, streaming drag-style video manipulation can animate any video frame during the generation process; however, non-streaming drag-style video manipulation struggles to animate intermediate framess as this con- flicts with the bidirectional nature of VDMs, which generates the entire video clip simultaneously. That means animating intermediate frames will break out the consistency of the original video clip.
non-streaming drag-style
REMARK 3. Tora and DragAnything can be directly integrated with existing autoregressive VDMs external modules animate generated video frames. However, they indeed cannot realize stream- ing control over autoregressive VDMs as they do not alter the original generation direction of VDMs. Differently, DragStream modifies the latent embeddings of autoregressive VDMs performing iterative latent region optimization, thereby enabling streaming control over the video generation process by propagating the information of modified latent code through a sliding context window.
The above analysis demonstrates the importance of advancing existing approaches from non- streaming to streaming drag-style manipulation, highlighting the significant application value of our REVEL task!
E MORE VISUALIZATION RESULTS
In Figure 12 we provide more visualization results achieved by our DragStream approach our proposed REVEL task. These experimental results still consistently demonstrate that our method can achieve high-quality streaming manipulation over the outputs of VDMs, including both Edit Editin, in and Animation with the effects of drag operations such as translation ("Trans"), deformation ("Defor"), and rotation ("Rot"). For more results, please refer to our anonymous project page, which is provided below the abstract.
F VISUALIZED ANALYSIS OF SWITCHABLE FREQUENCY
In Figure 1 we investigate the influence of switchable frequency used in SFS during latent region optimization. From the figure, the of only low-frequency ("w 0.2") information easily leads
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(Antratoon: Trans]
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[Animation: 3D Rot]
[Editing: Defor]
[Anination: Trans]
[Anination: Trans]
ion: Trans]
(animation: Trans]
[Animation: Rat]
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[Anination: 30 Rot:]
AAnination: 3D Rot
[Animation: 3D Rot
[Editing: Rat]
Editiing: Trans]
[Anination: Trans]
[Editing: Defor]
[Anination: 20 Rot:
Aniination: 20 Rot]
[Animation: Trans]
[Animation: Trans]
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Figure 12: More visualization results achieved by DragStream the REVEL task. Note that Editing produces only one video frame, but we insert an extra subsequent frame to maintain layout consistency with Animation
to scene blurring and unnatural object shape variation. Meanwhile, the high fighuffryeency information inherently contained in original images ("w = 1") causes noticeable artifacts in generated videas. In contrast, proposed switchable frequency strategy balance frequency components, effectively
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Lnnimation: Trans]
Streaming generation
Artifacts
w-1
0=8.2
Switchable
[Animation: Trans]
n=11
Blur
0-0.2
Swå Schtbe
Artifacts
[Animation: Trans]
w-1
Unnatural Shape Varlation
w-0.2
Switchable
Figure 13: Visualization analysis of switchable frequency. indicates cutoff frequency.
suppressing artifacts and blur while maintaining object shape consistency. These results confirm that our SFS strategy is crucial for achieving high-quality streaming video manipulation.
G VISUALIZED ANALYSIS OF GAUSSIAN FILTERING MAP
Figure 14 illustrates the effect of Gaussian filtering maps on streaming drag-style video manipula- tion. Without the use of Gaussian filtering map ("w/o CSS") to constrain the latent optimization, back-propagated gradient maps may be leaked to irrelevant regions, thereby resulting in artifacts in generated video frames (e.g.. distortions around non-targe areas). In contrast, with Gaussian filtering maps ('w/ CSS"), gradients are constrained to focus the most important target region, therefore suppressing their interference to surrounding areas and effectively improving video quality.
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Streaming generation
[Animation: Trans]
w/o css
Back-
propagated
Gradient map
[Animation: Trans]
w/ cSs
Back-
propagated
Gradient map
Ganssian filtering map
Figure 14: Visualization analysis of Gaussian filtering map.
H ABLATION STUDY ON OBJECTIVE FUNCTION LTOT
Table Ablation study on the objective function used in latent region optimization.
Experiment
ObjMC (4)
FVD (4)
FID (J)
DAI (L)
w/ Laec Leer
26.12 w/o Lest
596.51
25.16
0.0545
20.87
949.06
33.55
0.0509
w/o Laec
90.39
301.74
14.11
0.1337
In Table we provide ablation studies for the objective function used during latent region opti- mization. As can be seen from the table, removing the reconstruction loss Leee causes an obvious performance drop in ObjMC and DAI, which indicates that objects handle regions are not suc- cessfully dragged to target points. Although removing the constraint term Cest leads to slight im- provement in ObjMC and DAI, it causes significant degradation FVD and FID. This because, without Lest, non-editable regions are severely affected by the latent region optimization, resulting noticeable artifacts in generated videos. These artifacts significantly degrade the overall video quality, leading to substantial decline in FVD and FID scores. This observation underscores the importance of Leet in maintaining the integrity of non-editable regions and ensuring high- sigh-tuality video generation.
COMPATIBILITY OF DRAGSTREAM
In principle, our DragStream method model-agnostic and can be seamlessly integrated with dif- ferent autoregressive VDMs. To demonstrate this, we additionally apply our method to the recent autoregressive VDM. CausVid Yin et al. (202)). As can be seen from Figure 15 it can still achieve high-quality streaming drag-style video manipulation, enabling both Editin iniiiig and animat ion with fine-gre iinengrained drag operations such as translation (Trans), deformation ("Defor"), and rota- tion ("Rot"). These results demonstrate the effectiveness of proposed method again, and also
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[iditing: 20 Rat]
[Animation: Trons]
Streaming generation
[dditing: 20 Rot]]
[Anination: Trans]
[Animat ion: Trans]
[Anination: Trans]
[Anima tion: 20 Rot
Anninttion Trans]
Aniimat ion: 30 R
[Ed1 [Editing: Defor]
Edd ng: Rot]
[Anination: Trans]
[Editing: Defor]
[Animation: Trans]
[nnimation: 20
[Editing: Trans]
Annimattio::
[Editing:
[Editing: Defor]
[Anination: Rot]
Figure 15: Visualization results achieved by our DragStream approach based on CausVic Yin et al. (2025).
highlight its potential versatile training-free solution for streaming drag-style video manipula- tion across different VDM backbones. For more visualization results, we encourage readers to visit our anonymous project webpage, provided below the abstract.
J DRAG-STYLA OPERATION TYPES
Currently, DragStream supports both editing and animating video frames, with user-specified drag effects including translation, deformation, and rotation. For both editing and animation tasks, consider the following rules: i) translating object along trajectory be achieved by
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moving its entire region (as shown in fourth row from the bottom of Figure 12 the cup translation can be achieved by moving its entire region): ii) deforming object shape can be realized by tramslating only its edge region (an example is shown in the third row from the bottom of Figure 12); tii) object 21) rotation can be realized by using a planar rotation transformation (an example is given in the third row from the bottom of Figure (2); and iv) 3D rotation
be regarded translating sub- region of the object, assisted by the inherent prior knowledge of VDMs (as shown in the second row of Figure 12 the 3D rotation of the man't face can be realized by moving face sub-region).
K
WHY NEED CONTEXT FRAMES?
In section argue that the context frames sources of disturbance during streaming drag-style video manipulation. However, we also emphasize that context frames provide crucial visual cues that are essential for subsequent video generation. In Figure T6 we provide visualization analysis to illustrate the importance of context frames. As shown in the figure, without context frames ("w/o context"), the generated video frames become totally blurry and unnatural. Besides, the foreground object and background scene are completely changed to be inconsistent with the original video. In contrast, with context frames ("w/ context") and our SFSO mechanism, the generated video frames preserves the appearance and structure of the foreground object, well as the background scene, while achieving high-quality drag-style manipulation. These results firmly demonstrate that context frames provide indispensable visual cues for streaming drag-style video manipulation.
[Animation: Trans]
Blur
Background Change
w/o context
w/ context
[Animation: 2D Rot
Blur
Appearance Change
w/o context
w/ context
Figure 16: Visualization analysis the importance of context frames.
L ADDITIONAL VIDEO RESULTS IN SUPPLEMENTARY MATERIALS
We present additional video results in our supplementary material 'supp-material-1676 ing 9 videos, which provide a more intuitive demonstration of the results achieved by our approach. For more visualization results, we recommend readers again to visit our anonymous project web- page: DragStream Demo
includ-
M ROBUSTNESS ANALYSIS OF HYPERPARAMETERS
In this section, we analyse the robustness of several hyperparameters in our DragStream.
Analysis of L,, The hyperparameter Ln de- termines how many neighboring latent embed- dings are used to rectify drifted latent distribu-
Table 4: Analysis of the hyperparameter L..
Experiments
ObjMC (L)
DAI (L)
FVD (L)
Ln 0
45.09
0.093
608.63
23
Ln = 3
25.88
0.052
570.41
Ln = -9
23.05
0,051
552.39
18
24.37
0.051
554.25

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Published as a conference paper at ICLR 2026
tion during performing ADSR. The results in Table shows that our method is robust to the hyperparameter L.- When varying the value of T. from to 18, our DragStream exhibits consistently stable performance. By contrast, discarding ADSR (i.c., Ln = 0) leads to signif- icant performance degradation, which demonstrate the effectivness of our ADSR again.
Analysis of a. The hyperparameter a deter- mines the shape of the Gaussian filtering map. In Table 5] we provide an analysis of a. On one hand, similar the hyperparameter L, our DragStream still achieves stable performance when varying the value of a from 2 0.5. These results demonstrate that our approach is robust to the hyperparameter a. the other hand, the removal of the Gaussian filtering map ("woo CSS") results in an obvious performance drop, highlighting the importance of using the Gaussian filtering map to guide the model toward critical areas during drag-on oriented manipulation.
Table 5: Analysis of the hyperparameter
Experiments
ObjMC (L)
DAI (4)
FVD (1)
w/o CSS
26.12
0.061
25.16
2.0
24.70
0.054
24.52
a = 1.5
23.50
0.052
24.17
1.0
23.65
0.053
24.04
0.5
24.52
0.055
23.65
During latent region opti- mization, we let the model randomly switch among set of predefined cutoff frequencies w E {0.2, 0.4, 0.6, 1}, thereby preventing the high-frequency components from dominating the drag process and introducing artifacts or un- natural results. In Table @ we show that our method is robust to the set of cutoff frequen-
Analysis of
Table 6: Analysis of the hyperparameter w.
Experiments
ObjMC (4)
DAI (1)
FVD (1)
00.1, 0.8)
24.27
0.054
584.21
0.1, (1)
24.05
0,054
578.65
w E 0.61, (1)
23.99
0.053
558.97
(0.,,0.4,0.6,11
23.05
0.051
552.39
cies. As can be seen from the table, despite that the low-freque wwwffrequency component is randomly selected from continuous range [0, 1, 0.8), 1,, or
[0, 1, 0.6). our method still produce stable performance.
Analysis of I. Our DragStream is based on an iterative optimization scheme. In Table we investigate the influence of the hyperparam- eter I. The experimental results in the table demonstrate the robustness of our approach to hyperparameters again. For example, despite setting I with small value, such as / 3 or 2, our approach still exhibits robust performance in streaming drag-style manipulation, which is significantly better than that of the baseline without using our DragStream (i.e., 0).
Table Analysis of the hyperparameter
Experiments
FID
ObjMC (L)
DAI (L)
I = 0
14.11
90.39
0.133
2
25.88
27.67
0.054
1 = 3 24.17
24.55
0,053
= 23.72
23.05
0.051
N RESULTS ON CONTROLLING BIDIRECTIONAL MODELS
Although our DragStream designed for autoregressive VDMs, it can also be applied to bidirec- tional VDMs to control their generation process via drag-style control signal. From the results in Figure [7] we can see that our method can successfully guide the bidirectional model Wan-2.1 to generate videos that conform to drag-style conditions specified by users.
O DISCUSSION OF PROMPT-DRAG CONFLICTS
In Figure 18
we further investigate an interesting scenario in streaming drag-style video manipula- tion where drag operations conflict with text prompts. We observe that when such conflicts occur, vDMs consistently follow drag instructions specified by users during generation. This is because DragStream revises latent embeddings explicitly. For example, shown in the figure, al-
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Published as a conference paper at ICLR 2026
[Animation: Rot]
[Animation: Trans]
[Animation: Trans]
Figure 17: Results of controlling the video generation of the bidirectional model Wan2.1 via drag-style instructions using our DragStream approach.
Conflicted Text [Anination: Trans]
candle positioned on the left side
[Anination:
Trans]
the vehicle stays tightly the right side
[Animation: 3D Rot] move the sight to the right
... his eyes are fixed on the left side...
[Animation: 30 Rot] move the sight left
... she looking right side
Figure 18: Visualized case study on prompt-drag conflicts.
though the text prompt requires the car to remain the right side, our DragStream successfully drag the car toward the left side of the path.
P ADDITIONAL RESULTS ON LONG VIDEO GENERATION
In this section, provide additional streaming drag results of our DragStream in long video gen- eration. As shown in Figure 19 despite accumulated errors remain challenging issue for current autoregressive VDMs, our method can still effectively realize drag-base based manipulation in 5s, 10s, and 20s. Importantly, compared with the original videos generated by Self-Forcing Huang et al. (2025), drag operations introduced by our DragStream the drag-based manipulation process or in subsequently generated video frames after the manipu- lation. For example, in the second case, when we manipulate the frame at 9s (i.e., dragging the woman's face to the left), the animated video clip maintains the same quality as that produced by Seff-Forcing. Moreover, after the manipulation, our DragStream can continue to preserve the same quality Self-Foreing in the subsequently generated frames from 11s to 20s. These experimental sults firmly demonstrate that the effectiveness our DragStream approach in streaming long- -ideo generation.
not degrade video quality either during
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Published as a conference paper at ICLR 2026
Us
Generated Vidko
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6 Subsequently Cenurated Video
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Figure 19: Additional results of our DragStream on long video generation.
Q LONG-DURATION DRAG-ORIENTER MANIPULATION
In this section, we further investigate the effectiveness our ADSR strategy in long-duration drag- oriented video manipulation. As shown in Figure 20 performing drag-style manipulation without ADSR ("w/o ADSR + Drag") results in severe latent distribution drift, which leads to noticeable degradation in video quality compared with the baseline without drag-based manipulation ("Ref + w/o Drag"). In contrast, our ADSR ("w/ ADSR + Drag") can effectively suppresses the latent when drag operations
distribution drift issue video generated by our DragStream ("w/ ADSR Drag") significantly better than that of "w/o ADSR Drag", further demonstrating the effectiveness of DragStream approach.
sustained for 20s. Consequently, the
26

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Published as a conference paper at ICLR 2026
CON Co
Continuausly Drag
an I012 Srar
2s (28 frase)
5s (44 Frase)
11s (92 frase)
14s (115 frans)
18s (164 Frane)
28s (188 Frans)
Continuausly Drag
wil drae
2s (20 frave)
Ss (44 frane)
11s (92 frane)
14s (115 frone)
18s (164 frone)
285 (188 frane)
Ret 1o0o
fraxe)
frase)
frane)
14s (115 frane)
(164 frane)
(188 franc)
Figure 20: Analysis of ADSR in long-duration drag-oriented manipulation.
[Animation: Trans]
[Animation: Trans]
cup moves out
cup re-enters
moves out
re-enters
Figure 21: Streaming drag with object leaving and re-entering.
R OBJECT LEAVING AND RE- RE-ENTERING
In addition object occlusion and re-emergence discussed in Section 5.3 we also evaluate our DragStream in another interesting scenario objects leaving and re-entering the view. As shown in Figure 2 when an object dragged to move out of the frame and later back into view, DragStream effectively preserves its appearance and structure, producing high-quality results of drag-based ma- nipulation. Actually, the re-entering process is essentially no different from the standard drag-based manipulation. We just need to save the latent features of object before it moves out of view, and then reconstruct is along eser-given trajectory via latent optimization.
scenario-objeets
27


Reviews:
[{"review_id": "H9CgsQRPNj", "reviewer": "Reviewer_XFrK", "summary": "This article introduces a novel task, REVEL, and a corresponding solution, DragStream.\n\nREVEL focuses on streaming-based video generation, enabling users to interactively modify any part of the frame at any time through dragging.\n\nDragStream is a training-free approach that consists of two key modules: (1) ADSR, which addresses latent distribution drift, and (2) SFSO, which mitigates the dominance of high-frequency details in the context during the drag process.\n\nExtensive quantitative and qualitative experiments demonstrate the effectiveness of the proposed method.", "questions": "I think the logic and contribution of this paper is clear. What I am most curious about are three points: the efficiency of DragStream, its performance in long video generation, and which hyperparameters are robust versus which are sensitive.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "- This paper is written clearly and is easy to follow.\n- It introduces a new task, REVEL, which focuses on streaming-based video generation, allowing users to interactively modify any part of the frame at any time through dragging.\n- To address REVEL, the paper proposes a training-free method called DragStream, which consists of two core modules: ADSR and SFSO.\n- ADSR addresses the issue of latent distribution drift by normalizing the current latents through statistical analysis of the mean and variance of neighboring latents, thereby adjusting the distribution.\n- SFSO tackles the influence of high-frequency details in the context on the dragging process. Specifically, it uses Switchable Frequency-domain Selection (SFS) to control the amount of high-frequency information and Criticality-driven Spatial-domain Selection (CSS) to focus modifications on the handle region, reducing the impact on the global background.\n- Quantitative and qualitative ablation experiments demonstrate the effectiveness of both ADSR and SFSO.\n- Section D further discusses the differences between streaming/autoregressive models and non-streaming models.", "weaknesses": "- The efficiency of iterative latent region optimization (Sec. C.1) is stated with $I=4$, but I would like to know the runtime or complexity analysis. Since streaming-based generation is often used for real-time applications, can DragStream maintain sufficiently fast performance?\n- For long video generation (e.g., exceeding 5 seconds, requiring multiple extensions), errors from autoregressive generation tend to accumulate over time. Is DragStream still effective in such cases where longer durations are involved?\n- Does the denoising timestep ($T^′$) have a significant impact on DragStream? Is the choice of ($T^′$) dependent on the specific (distilled) video model?\n- What happens if there is a conflict between the text prompt and the drag signal? For instance, if the text prompt directs a car to turn right while the drag signal indicates a turn to the left.\n- Although Section D discusses non-streaming scenarios, It may also be worth validating DragStream on bi-directional models such as Wan2.1/2.2. If it proves effective in such cases, the training-free approach would be highly accessible to the community. At the very least, a discussion on the design considerations for such models would be valuable.\n- My understanding may be incomplete, but it seems there could be a contradiction between on-the-fly editing and streaming generation. Typically, users might prefer to view the generated results first, then decide to modify a specific frame if it does not meet their expectations. However, in cases where the text prompt is incapable of achieving the desired outcome, users might apply drag operations before seeing the result. Could the authors elaborate more on the user cases of REVEL?\n\n**Minor point**: The terms \"ADSR\" and \"SFSO\" could potentially be replaced with more intuitive, semantic meaningful names.", "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": "fBhexyPLrT", "reviewer": "Reviewer_HUjr", "summary": "This paper introduces REVEL (stReaming drag-oriented interactive vidEo manipuLation), a new task for fine-grained, interactive control of streaming video generation. The authors propose DragStream, a novel training-free method that enables users to drag any part at any time by manipulating objects within autoregressive video diffusion models. DragStream addresses two key challenges: it uses an Adaptive Distribution Self-Rectification (ADSR) strategy to prevent the accumulating \"latent distribution drift\" caused by user edits, and it employs a Spatial-Frequency Selective Optimization (SFSO) mechanism to mitigate \"context interference\" from previous frames. This allows users to perform complex translation, deformation, and rotation operations on streaming video content.", "questions": "Please see weaknesses.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 1, "contribution": 3, "strengths": "The method's primary strengths lie in its practical application and thorough empirical validation.\n\n- The ability to perform streaming, drag-oriented control is a highly useful and interesting application for interactive video generation, moving beyond static, one-off edits.\n\n- The visual results, as demonstrated in the supplementary videos, are impressive and clearly show the method's effectiveness in manipulating video content smoothly and coherently across frames.\n\n- The paper is supported by a strong set of quantitative experiments and a detailed ablation study (e.g., Figures 6 & 7) that clearly validates the contribution and necessity of each proposed component (ADSR and SFSO).", "weaknesses": "- I think the main major weakness is the presentation of the paper. The core concepts, which appear to be intuitive, are obscured by an extremely dense and overly complicated notational system. This heavy use on complex notations makes it very difficult for the reader to build intuition and grasp the main contributions without significant, unnecessary effort.\n\n- In the Spatial-Frequency Selective Optimization (SFSO) mechanism, the Switchable Frequency-domain Selection (SFS) strategy (Prop 3) is key. The paper states the cutoff frequency $\\omega$ is randomly selected from {0.2, 0.4, 0.6, 1} (an ablation study is also done on fgure 7). Could the authors elaborate on the intuition and provide more explanation?\n\n- For the Adaptive Distribution Self-Rectification (ADSR) strategy (Prop 2), the latent code is rectified using the statistics (${\\mu}_{T'}$, ${\\sigma}_{T'}$) from preceding neighboring frames. How is the window size for these neighboring frames determined, and how sensitive is the model to this hyperparameter? Is there a trade-off between a larger window (more stable statistics but potentially less relevant) and a smaller one (more relevant but noisier statistics)?", "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": "OnPLH6XnBT", "reviewer": "Reviewer_gQWk", "summary": "This paper introduces a new task, streaming drag-oriented interactive video manipulation and a corresponding training-free framework called DragStream. The goal is to enable fine-grained, real-time video control via drag-style user interactions, allowing users to modify generated videos anytime on any object. To address two key challenges—latent distribution drift and context interference—the paper proposes two techniques: Adaptive Distribution Self-Rectification (ADSR), which stabilizes latent features using neighboring frame statistics, and Spatial-Frequency Selective Optimization (SFSO), which mitigates contextual noise through selective propagation in spatial and frequency domains.", "questions": "Most of my concerns have been discussed above, while I still have several minor questions as follows,\n\n* Can the authors provide more insight into how ADSR interacts with the diffusion denoising schedule? For example, does applying rectification at different timesteps change the generation stability?\n\n* How does the framework handle object occlusions or re-appearance during dragging? Is there any mechanism to prevent artifacts when an object moves out of view and re-enters the frame?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* The work defines a clear and meaningful new task that formalizes streaming, interactive drag-style manipulation, a setting not explicitly covered by prior works.\n* The proposed DragStream method is conceptually elegant and computationally efficient, requiring no finetuning while offering plug-and-play integration with autoregressive diffusion models.\n* The paper is well-written, and the figures effectively convey both the problem and the proposed solution.", "weaknesses": "* Evaluation is limited to adapted baselines that are not optimized for streaming settings, which makes it difficult to judge true generalization advantages.\n* The framework depends heavily on empirical hyperparameters, e.g., cutoff frequencies, Gaussian filtering spread, without deeper theoretical justification or convergence analysis. And these empirical hyperparameters may harm its generalization ability.\n* The paper focuses on short clips and relatively simple object manipulations. However, in real streaming setups, drag edits often accumulate over long sequences. The manuscript does not examine whether distribution drift resurfaces over extended temporal spans or whether contextual filtering introduces temporal lag or smoothing artifacts.\n* Although the method is claimed to be “training-free,” the iterative latent-region optimization may still incur noticeable inference overhead. The paper lacks detailed runtime analysis, such as per-frame latency under streaming conditions, GPU memory usage, or responsiveness to real-time drag input.", "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": "jSygM4UVBY", "reviewer": "Reviewer_y2YG", "summary": "This paper presents the REVEL task, designed to enable users to edit the future generation content by conducting drag-style editing on the existing last frame. It unifies the editing and animation tasks under one framework, allowing for user-specified translation, deformation, and rotation effects on general objects during generation. This paper proposes the DragStream framework to solve the REVEL task. DragStream contains ADSR to handle distribution shift and SFSO to mitigate context inference from previous frames.", "questions": "I have the following questions for the authors of the paper to refine their paper further:\n\n1. I'd like to know if there are any methods the authors can propose to propagate editing into previous frames, not just the frames to be generated.\n2. Since the paper only provides three ways of editing, I wonder whether it can support general editing instructions.\n3. I wonder how, why, and under what conditions the DragStream framework would fail.\n4. What is the average time to process a video?", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "The strengths of the framework can be summarized as follows:\n\n1. Interesting task proposed: The REVEL task that allows the user to modify anything on anything that unifies drag-editing and animation.\n2. Both ADSR and SFSO are well-structured and decently presented methods\n3. The experiment and the appendix section provide a relatively comprehensive view of the performance of the DragStream framework", "weaknesses": "Despite the paper presenting an interesting task and relatively comprehensive experiment results. However, the weakness of this paper can not be ignored. The weaknesses are as follows:\n\n1. **Unclear and impractical task definition:** Despite being conceptually novel, I think the REVEL task is not clearly or realistically defined. The task claims to have the effect of modifying the generated videos at any time and for anything. This creates an internal contradiction in the task definition and leads to practical limitations. In particular, editing only future frames can cause severe temporal discontinuities between past and modified content, violating one of the central goals of video generation—temporal consistency. This is my major concern to the paper.\n2. **Overclaiming effect:** The paper claims that the REVEL task allows users to edit anything at any time. However, the DragStream framework can not support that. It only supports three ways of editing (namely translation, deformation, and rotation), but is unable to perform broader semantic or structural edits. Moreover, it operates only during the generation process, not on completed videos.\n3. **Lack of failure case studies: T**here are no studies of how, why, and under what circumstances the framework would fail. This makes it hard to understand the effect of the framework comprehensively.\n4. **No latency analysis:** Since the proposed framework requires the user to edit during video generation, the computation time cost is vital. However, the paper does not report latency, runtime, or GPU cost for editing a single video, which are essential for assessing the practicality of interactive systems.\n5. **Complexity in notation:** Although the methodology is detailed, the notation used in the paper is unnecessarily complicated, which hampers readability and accessibility for a broader audience.", "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)