Real-Time Motion-Controllable Autoregressive Video Diffusion
Authors: Kesen Zhao
Jiaxin Shi
Beier Zhu
Junbao Zhou
Xiaolong Shen
Yuan Zhou
Qianru Sun
Hanwang Zhang
Venue: ICLR 2026 Poster (2026)
Keywords: Autoregressive Diffusion
Controllable Video Generation
Reinforcement Learning
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Real-time motion-controllable video generation remains challenging due to the inherent latency of bidirectional diffusion models and the lack of effective autoregressive (AR) approaches. Existing AR video diffusion models are limited to simple control signals or text-to-video generation, and often suffer from quality degradation and motion artifacts in few-step generation. To address these challenges, we propose AR-Drag, the first RL-enhanced few-step AR video diffusion model for real-time image-to-video generation with diverse motion control. We first fine-tune a base I2V model to support basic motion control, then further improve it via reinforcement learning with a trajectory-based reward model. Our design preserves the Markov property through a Self-Rollout mechanism and accelerates training by selectively introducing stochasticity in denoising steps. Extensive experiments demonstrate that AR-Drag achieves high visual fidelity and precise motion alignment, significantly reducing latency compared with state-of-the-art motion-controllable VDMs, while using only 1.3B parameters.

REAL-TIME MOTION-CONTROLLABLE AUTOREGRES-
SIVE VIDEO DIFFUSION
Kesen Zhao Jiaxin Shi
Beier Zhu
Junbao Zhou'
Xiaolong Shen*
Yuan Zhou'
Qianru Sun Hanwang Zhang'
*Nanyang Technological University. *Xmax.AI Ltd, 'University of Science and Technology China 'Zhejiang University, SSingapore Management University kesen0020c. ntu, edu. sg
ABSTRACT
Real-time motion-controllable video generation remains challenging due the inherent latency of bidirectional diffusion models and the lack of effective autore- gressive (AR) approaches. Existing AR video diffusion models limited to sim- ple control signals or text-to-video generation, and often suffer from quality degra- dation and motion artifacts few-step generation. To address these challenges. we propose AR-rrag, the first RL-enhanced few-step AR video diffusion model for real-time image-to-video generation with diverse motion control. We first fine- tune a base 12V model to support basic motion control, then further improve it via reinforcement learning with a trajectory-baset reward model. Our design pre- serves the Markov property through Self-Rollout mechanism and accelerates training by selectively introducing stochasticity denoising steps. Extensive ex- periments demonstrate that AR-Drag achieves high visual fidelity and precise motion alignment, significantly reducing latency compared with state-of-the motion-conttollable VDMs, while using only 1.3B parameters. Additional visual- izations can be found on our project page: https: teess///essnziinn /ww////eeezaa....e www.hhaa...htth........ titu... io/AR-Dree gita.b..tuu..... io/
INTRODUCTION
Video diffusion models (VDMs) have made remarkable progress with bidirectional diffusion trans- formers (DiTs), which denoise all frames simultaneously (Kong et al. 2024 Villegas et al.) 2022] Wan et al. 2025 Yang et al.) 2024, Wang et al., 2025a As shown in Fig. ()), they allow future information to influence the past and require generating the entire video frames jointly. All existing motion-comee controllable VDMs are dominated by this bidirectional design. As a result, generation de- layed until all control inputs are specified, causing high latency and disallowing real-time adjustment of controls, e.g., sequential motion cues that evolve as the video unfolds. In contrast, autoregressive (AR) VDMs (Yin et al., 2025 Gao et al., 2024 Gu al., 2025: Lin al.) 2025) generate videos sequentially, making them naturally aligned with real-time controllable video generation.
Despite being well-suited real-time control, existing AR VDMs primarily target text-to-video (T2V) generation and remain limited in the more challenging image-to-video (I2V) scenarios (Yin) al. 2025 Huang al. (025), or only explore simple control signals such as pose or camera motion (Lin et al. 2025 Shen et al.) 2024). Controllable AR VDMs face two major challenges: (1) quality degradation and motion artifacts caused by error accumulation, especially for few-step models. (2) richer control modalities such as trajectories bounding boxes (Zhang et al.) 2025), that broaden the action space and require stronger generalization. To the best of our knowledge, our AR-Drag (Fig. [b)) is the first AR VDM enabling real-time motion control with visual quality competitive with bidirectional ones. As shown in Fig, [c), AR -rr--rrgg achieves substantially lower latency while maintaining superior FID compared with of of of-the-art notion-controllaan VDMs.
*Work was done during an internship XIax.AI.
I Project leader.
I "oorrsponniig author.

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Signes
Hottry
Ourprr
230
225
DrogNLWWA
Birdiectional VDM
Autoregressive VDM
Latesty
Drogenything
Begenancnn
AR AR-Drog
Pagenerate
0-
High Latency!
Merkry
FID
(o) Bidirectional VDMs Inference
AR VDMs Inference
Latency Motion-controllable VDMs
Comparison for coation-cont video generation. (a) Bidirectional VDMs denoise all frames jointly; motion control can be adjusted only after all frames are generated, causing high latency. (b) In contrast, AR VDMs generate frames sequentially: motion control can be updated frame by frame and, if unsatisfactory, regenerated on the fly, enabling real-time adjustment. (c) Our method achieves significantly lower latency while maintaining superior FID performance.
Figure 1:
In response the challenges, reinforcement learning (RL) is natural fit. Unlike supervised learning, which enforces pixel-level reconstruction and limits the model to the training distribution, RL explores the action space and optimizes policies via tria--anderroo. enabling strategies that gen- eralize beyond seen data. Recent work built on GRPO (Guo et al. (025), such as DanceGRPO and FlowGRPO (Xue et al., 2025; Liu al.) 2025), demonstrates the effectiveness of RL for bidirec- tional flow-matching models in text-to-image (T2I) generation. However, extending GRPO to video generation raises several challenges: (1) ensuring the Markov property, since typical AR VDMs condition on ground-truth frames during training rather than self-generated ones, breaking the MDP formulation; (2) handling the long decision process of video generation, where exploration across the entire decision chain becomes prohibitively expensive; (3) the lack of well-defined reward mod- els tailored to controllable video generation.
To address these issues, propose AR- ARR-rrag, RL-enhanced few-step AR VDM for real-time cottinlconttoollalee 12V generation. Specifically, we first fine-tune the Wan2.1-1.3B (Wan et al., 2025) 12V model on curated control-aware data enable basic motion control, and then further improve it through reinforcement learning. To preserve the Markov property, we introduce Self- Rollout, training on model- histories to align with AR inference. To keep long-horizon exploration tractable, we adopt selective stochasticity: a single randomly chosen denoising step uses an SDE update, while all remaining steps follow the deterministic ODE solver. In addition, we design a rrajectory-base reward model to enforce fine- fine-grained control over complex motion signals.
Our contributions are threefold: (1) We propose AR- AR-Drag, the first few-step AR VDM capable of (2) We introduce RL-based training for AR VDM and design a trajectory- based reward model tailored to fine ane-grained motion alignment. (3) We conduct extensive experiments showing that AR AR-Drag significantly improves both visual quality and controllability, despite using only 1.3B parameters.
real-time controllable 12V generation.
RELATED WORKS
Controllable video generation. Textual modality have been extensively studied (Zhu et al. 2025b
2004bba a), whereas motion control in VDMs remains relatively underexplored. Early methods (Jeong
al.] 2024 Wang al. 2023 Zhha al. 2024) achieve motion control by injecting motion signals into VDMs, yet their capability is restricted to reproducing pre-defined dynamics. Recent works
(Geng et al. 2025 Ma et al. 2024 Mou et al. 2024 Shi et al. 2024 Wang et al. 2024 Yin et al.,
2023 Zhang et al. 2025 Wu et al. 2024 Wang et al. 2025b Zhou al. 2025) leverage explicit control inputs such as motion trajectories, offering greater flexibility. For example, DragAnything
(Wu et al. 2024) leverages object masks for entety-level control, and Tora (Zhang et al. 2025) introduces trajectory conditioning into a DiT framework. However, all these methods are non- autoregressive and therefore unsuitable for real-time interactive control.
2

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Real-time video generation. Video diffusion models typically adopt bidirectional attention mech-
anism (Blattmann et al. 2023a Brooks et al. 2024 Ho et al.) 2022 Kong et al. 2024 Villegas
et al. 2022 Wan et al.
2025| Yang al. 2024). While effective for quality, this design requires
jointly denoising all frames of video, limiting their applicability to real-time interactive. Autore-
gressive models (Hu et al. 2024 Jin al. 2024 Yin et al. 2025) Gao al. 2024 Gu al. 2025
Li al. 2025c). in contrast, generate tokens sequentially, making them inherently better suited for real-time controllable video generation. Some attempts (Yin et al. 2025 Lin et al. 2025 Yang
et al. 2025) distill multi-step VDMs into few-step autoregressive VDMs using distribution match- ing distillation (Yin al. [2024bla a or consistency distillation (Song et al. 2023 Song Dhariwal 2023). However, AR VDMs still exhibit train-test mismatch, making them prone to error ac- cumulation across frames- particularyy in few-step models. To mitigate this, some works (Chen) et al., 2024 Teng et al. 2025 Sun et al. 2025) propose progressive noise schedules that gradu- ally increase noise from early to later frames, partially alleviating error accumulation. However, they neither close the train-test gap nor support real-time interaction, since future frames must be pre-generated before the current frame is rendered, introducing latency and limiting control effec- tiveness. Self-Forcing (Huang et al.] 2025) narrows the train-test test gap and improves stability by unrolling autoregressive generation during training. conditioning each frame on previously gener- ated outputs rather than ground truth. However, does not strictly follow the autoregressive chain rule and leaves residual discrepancies (see Sec. [.2). In contrast, our Self-Rollout strategy strictly adheres to the chain rule and aligns training with inference, providing a more principled formulation for integration with reinforcement learning.
Alignment for diffusion model. Reinforcement learning (RL) post-training has demonstrated strong effectiveness in MILMs (Li et al., 2025a Peng et al. 2025 Wu et al. 2025) and has also been increasingly adopted in video generation. Existing approaches include sealar reward fine-
tuning (Prabhudesai et al. 2023] Clark et al., 2023 Xu et al. 2023, Prabhudesai et al. 2024). Reward-Weighter Regression (RWR) (Peng et al. 2019 Lee et al. 2023 Furuta al. 2024). and
Direct Preference Optimization -DPO)-based methods (Rafailov et al. [203 Wallace et al. 2024 Dong et al.] (223).. However, policy gradient methods (Schulman et al. 2017]]
1 Fan et al. 2023 often
suffer from instability. To improve stability in generative modeling, recent works such Dance- GRPO (Xue et al. 2025) and FloWGRPO (Liu et al. 2025) extend GRPO to flow-matching models. Building this line of research, we extend GRPO to the I2V setting, achieving improved motion controllability while maintaining visual quality and efficiency.
3 METHOD
Our AR AR-Drag has two steps: In step (Section 3.2). we build real-time AR base model with basic motion control ability-aseeblle control-aware data, train bidirectional teacher, and distill to a few-step causal student; during distillation we introduce Self-Rollout to align training with AR inference. In step (Section 3.3). we treat AR video generation as an MDP and optimize with GRPO, designing selective stochastic sampling and reward to improve realism and motion control.
3.1 PRELIMINARY
Flow matching, Given a prior Pa (x) and target data distribution p1 (x). flow matching constructs an interpolating distribution (x). The sample trajectory x, follows the probability flow ODE:
dx:
dt vo(xi, (), Xo po-
(1)
The training objective minimizes the squared error between the predicted vector field vo and the ground-truth flow V:
(f) Exxxxllvo(xxx -1
(2)
where the target velocity field is = X
Flow-ODE to SDE. In flow-based probability models, the forward process is deterministic and follows an ODE: dx, = v,dt. To introduce stochasticity while preserving the same marginal distri-
butions, a reverse- time SDE formulation can be defined as:
dx, (vr(x:) bo² log (x:))) a dw,
(3)
3

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Nx DIT Blocks
Nix biT Blocks
Text Encoder
VAE Encoder
CLIP Encoder
Encoder
Enceder
Enceder
Tixstrp
Text
N
Text
Memory
Nuxt 2014
Attention
Update
(a) Training process of typical AR
(b) Training process with Self-Rollout
Figure 2: Comparison between typical AR VDMs and Self-Rollout. Self-Rollout faithfully follows the inference process during training, minimizing the train-test gap and naturally preserving the Markov property.
which leads to the update rule:
Xt+At = xx + [vo(x,, 1) + ox7 (1 (xva(xx))))]]. + ar v.VAr Ale.
(4)
Distribution matching distillation (DMD). DMD distills a multi-step teacher model into a few-step student model (Yin et al.) 2024b]a) by minimizing the KL divergence between suddent-generated distribution pa.t and data distribution distribution Pdata.t across randomly sampled time
E2 [T. (po.: [[Paaa.)))
(5)
3.2 STEP 1: FINE-TUNIAS A REAL-TIME MOTION- CONTIOLCONTTOLLLBLE BASE VDM
In Step 1, we build a base AR VDM with basic real-time motion control by (i) curating videos with control signals, (ii) fine-tuning a bidirectional VDM on this data learn motion control, (ii) distill- ing it into a few-step causal AR model for real-time inference with Self-Rollout which "Markovize" AR training and paves the way for GRPO in Step 2.
Data curation. We collect training corpus of real and synthetic videos featuring diverse motions. Control signals are obtained by generating keypoint trajectories with an automatic detector (Doer- sch et al. 2022) and retaining only samples that pass human verification. For challenging cases, such as occlusion or fast motion, we additionally curate a high-quality dataset that is fully anno- tated by human annotators. Our curated corpus encompasses rich spectrum of actions and visual galles-spanning humans, animals, and cartoons and includes videos varying resolutions and durations. making it well-suited for evaluating generalization across diverse scenarios. In addition, each video is accompanied by rich textual descriptions (both positive and negative prompts). Please refer Appendix C. for the details.
Bidirectional fine-tuning with motion-control. At m-th frame, we use three control signals
(c traj text
c.
m =0 O,
C.
{
(c clett
otherwise.
(6)
Here, c. is motion-trajectory embedding obtained by encoding the raw coordinate heatmap at frame with VAE encoder (Wan al., 2025). clxt encodes the textual signal, combining both positive and negative prompts. The text embedding is shared across all frames. At the initial frame 0), the reference image embedding ee is encoded by VAE encoder and CLIP visual encoder (Radford et al. 2021). For subsequent frames (m.00), we do not condition on a reference image (0) and inject Gaussian noise in its place.
The model is trained with the flow matching objective, extended to incorporate control signals:
(f) = E, E vo x²) - -v]
where denotes the full set of control inputs across the entire video, e.g., {Cmm m==0

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Distilling to real-time AR model. Following previous techniques (Huang et al. 2025 Yin al.
2023). we distill the bidirectional teacher model into a few-step student model by replacing bidirec- tional attention with causal attention. The student is further optimized with DMD Yin et al. 2024a) and adversarial losses (Goodfellow et al. 2020). Given a noise schedule T = (1o 7, IN
0), each frame is denoised over N steps, where N is significantly smaller than that in multi-step
VDMs, enabling real-time inference.
Self-Rollout:
Markovizing AR training. Although an AR VDM conditions on its own generated history at inference, AR training typically uses teacher forcing-each step conditions on ground- truth past frames rather than model outputs-creatinnn creating train-test mismatch (exposure bias) and breaking the Markov property required for RL. As illustrated in Fig. [2 (a), noise is added to the ground-truth frame, and the model predicts the corresponding vector field.
To address this issue, we propose Self-Rollout strategy, which maintains key-value (KV) memory cache storing previously denoised frames as causal context. As shown in Fig. | (b), frames are denoised sequentially from pure noise during training. Let Xm.n denote the mn-th frame at denoising step n. For the m-th frame, we randomly sample a denoising step ni denoise step-by-step from Xm.0 to Xm,.. and compute the DMD loss in Eq. (5) and adversarial loss. We then continue denoising from Xm,x to Xm.N step-by-step, updating the KV cache with the generated clean frame X,.NN In the self-generated KV cache rather than ground-
this way, subsequent frames conditioned truth history. In contrast, Self-Forcing (Huang et al.] 2025) updates the KV cache by collapsing the denoising trajectory from Xm,n Xm..N into single step. Our step-by-step Rollout more faithfully matches inference dynamics and naturally integrates with RL-based training.
3.3 STEP 2: REINFORCEMENT LEARNING ON AR VDM
Our Self-Rollout strategy (Sec. 3.2) "Markovizes" AR training by conditioning model-generated histories and the ODE-to-SDE conversion in Eg. 4 supplies the stochasticity. Taken together, these resolve the two obstacles to applying GRPO it requires an MDP and stochastic rollouts. In the sequel, we first set notations and formulate the MDP underlying video generation.
Notations. Consider video of M+1 frames, each denoised N steps. We denote the m-th frame at denoising step by Xm.n. Let X Xcm.N = (No.N..... Xm-1.N) } be the m already denoised clean frames and X>m.0 {xm+110 ... XM.o the unprocessed, boise-initialized frames. At state (m, n), the video snapshot is
Xm,,
X Xem,N fully generated
(Xm.n)
X x=m.0 S
being denoised
initial noise
The final clean video then X M.N Nu.N..... XM.N For autoregressive video generation, the denoising across frames produces trajectory
[Xo,0. Xu.1..... XI.N. X,,,, X1,1, XINN....
X M,0.
SM...XMa X MM...........................M.. XM.N
trajectory of frame
trajectory of frame 1.
trajectory of frame M
Video generation as MDP. The denoising process in VDM can be formulated Markov decision
process (MDP) (Liu et al. 2025 Xue al. 2025);
State: Sm,n (Cm, bn M,,,,, where Cm the control signals. The initial-state distribution is p(So.o) = p(c, lo, Xo.o) = p(Cn) 6(t-tp) - N O. I), i.e., the control co is drawn from its prior, is fixed to fo. and all frames start from Gaussian. 5(.) denotes the Dirac distribution.
Xm,n+1, i.e., the next denoised state of the m-th frame step n+11. The policy is parameterized by the VDM with 0:
Action:
am, -
am,n Xm,+t1 po( Ciny buy X X,.n).
(10)
where stochasticity is introduced through the ODE-to-SDE conversion in Eq.
Transition: (1) intra-frame transition. With in frame, the transition is deterministic given the current state and action: p(s Sm,n, am,n) = ö(s sm,++1). (2) inter-frame transition. When denoising of frame is complete (n = N), the state transitions to the initial state of the next frame m1
Sm+,.0 (Cm+11 lo Xm+110),
where X Xm,, by definition.
(11)
5

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Reward function: Rewards are provided only when a frame is fully denoised (n N):
R, am,n) - R( Xm, Cm) = [m N] (Rquality (XmaliyyXm.N)) + Reottoon (x,,N. Cml)
(12)
where 1 is the indicator function, Rquality measures perceptual fidelity and temporal smoothness and mosiion measures alignment with control signals. (We defer precise definitions to the sequel.)
GRPO for AR VDM. We extend GRPO framework to AR video generation. Under the MDP formulation, the AR VDM samples group of G videos {X
(1)
M,NJi=1 along with their trajectories
{r()g 12m1- The advantage of the i-th video is computed as:
(2) R( R(x N. C..) mean ([((x)) (3) m, Cm mm))
A0
(13)
sud(( (3) [R(x Cm) cm) )=11)
The GRPO objective is defined as:
LGRPO (TT) ee
fr mar (-1c)
G N min (0)4 n. clip(r m.n (0), - 1-,, +)) -BKIL x Fret
GMN
1=1 m=
(14)
where the importance ratio is: (0) pa x Xm.n, Cm) /p... (x (x Xm.,, cm).
Selective stochastic sampling. GRPO requires stochasticity for advantage estimation and policy exploration, which we introduce via the ODE-toSDDE conversion. However, in video generation the Markov chain extremely long, and applying SDE sampling at every denoising step induces very high variance in trajectory returns, which substantially increases the number of rollouts (G) needed for stable loss estimation and thus incurs prohibitive cost.
To balance exploration and efficiency, adopt selective stochasticity: a single denoising step ñ randomly chosen to follow the SDE formulation, while remaining steps stay deterministic under the ODE solver. This strategy injects sufficient randomness for effective RL training, while maintaining computational efficiency.
Reward design. We design composite reward that jointly evaluates visual realism (Raunlit) and motion controllability (Rmotion). For realism, we adopt the LAION Aesthetic Quality Predic- tor (Schuhmann 2022) denoted as faQ that assigns aesthetie score (1-5) to each image. The realism reward is defined as
Reuality (Xm,N) [xm,
(15)
For motion controllability, we employ Co-Tracker (Karaev et al. 2024) to first estimate the object trajectory tmj at frame m from the generated image and measure their alignment with the ground- traj The motion reward is defined as truth
Rmotioon (Xm,N Cm) X max(00, a - ||a cuai (1)),
(16)
where a is an offset, and À is the scaling hyperparameter.
3.4 DISCUSSION WITH EXISTING TECHNIQUES.
Our Self-Rollou eliminates this collapse entirely by continuing full step-by-step ancestral sampling using only the model's own predictions-ideniicc to inference, Combined with selective stochas- ticity (Sec. 3.3). we reduce the effective horizon by 5-20x while preserving exploration, enabling stable and effective GRPO training on autoregressive video diffusion for the first time.
Comparison with Self-Forcing Although our Self-Rollout strategy and Self-Forcing (Huang al., 2025) both address exposure bias by using self-generated context autoregressive video diffu- sion, they differ fundamentally in how the KV cache is updated after the supervised prefix. These differences critically impact alignment with inference-time dynamics and compatibility with rein- forcement learning objectives such GRPO.
By performing full step- steppp rollout instead of a single non-sequential collapse, Self-Rollout perfectly eliminates the rrin--iference distribution mismatch, provides clean sequential decision

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Table 1: Quantitative comparisons with motion-conttrollabee VDMs. Best results are bold.
Aesthetic Quality t
Motion
Motion Consistency ?
Method
Latency (s)
FID
FVD +
Smoothness t
DragNUWA
94.26
36.31
376.39
3.30
0.9759
3.71
traingg
68.76
38.13
367.74
3.22
0.9811
3.63
Tora
176.51
32.84
283.43
3.86
0.9855
3.97
MagicMotion
1426.37
30.04
230.53
4.01
0,9871
3.95
Self-Forcing
0.95
34.47
315.87
3.70
0.9920
4.06
AR-Drag
0.44
28.98
187.49
4.07
0.9948
4.37
process that GRPO can directly optimize, and-when combined with selective stochasticity sam- effeetively mitigates the extremely long-horizon problem. This enables successful applica- tion of GRPO to high-fidelit autoregressive video generation for the first time.
EXPERIMENTS
Implementation details. We implement base model with Wan2.1-1.3B-12 (Wan al. 2025 using a 3-step diffusion process in a frame-wise manner, denosing one latent at a time. To accommo- date varying resolutions, we define The KV cache is set to hold frames; when updating the cache, the oldest frame is removed if the cache exceeds this size. All training performed using the AdamW optimizer (Loshchilov Hutter, 2017) with a learning rate of 1 x 10 on 8 NVIDIA H20 GPUs. We do not adopt LoRA fine-tuning, as it may introduce long-tail performance degradation (Tia al. 2024 2025 Song et al., 2024). For evaluation, we curate new benchmark consisting of 206 video clips covering diverse motion trajectories and scene variations, specifically designed to assess motion controllability.
of bucket sizes and resize each video to its nearest bucket.
Metrics. We adopt standard metrics such as Fréchet Inception Distance (FID) (Seitzer 2020
Fréchet Video Distance (FVD) (Unterthiner et al. al, 2018), and Aesthetic Quality (Schuhmann, 2022) to quantitatively evaluate visual quality. To assess motion controllability, mentary measures: Motion Smoothness (Huang et al. 2024), which captures the stability of motion across frames, and Motion Consistency, which evaluates the alignment between control trajectories and the resulting motion dynamics, computed using our proposed reward model. We report first- frame latency calculated
employ comple-
single NVIDIA H20 GPU
indicator of real-tine real-time performance.
Baselines. We compare our method against strong open- source motion motion-guided guided VDMs, including
Drag (Yin et al. 2023), DragAnything (Wu et al. 2024). Tora (Zhang al.) 2025) and Magicmotion (Li et al. 2025b). Following prior work (Zhang et al. 2025), we improve DragNUWA by adoping motion trajectory design DiT-based architecture. Tora is the first one to apply DiT in this task, and MagicMotion further support complex trajectories- basee controls. Since no AR motion-con control 12V baseline available, we fine-tune chunk- wise AR VDM, Self-Foreing (Huang et al., 2025), which was originally designed for text-to-viee -ideo (T2V) generation. Specifically, we fine-tune Wan2. 1-1.3B-1122 following the Self- Celf-nFocing architecture and training procedure using the same datasets as AR-Drag. In this adaptation, the model denoises three latents simultaneously in each denoising loop to achieve effective motion controllability.
4.1 RESULTS
Quantitative comparisons. The overall performance comparisons are reported in Tab. [ leading to the following key observations: Our method significantly reduces latency. It requires only 0.44s, while bidirectional approaches such as Tora take 176.51s-less than 1% of their latency. For the
model MagicMotion. the latency is even higher at 1426.37 s. Thanks to the few-step distillation and causal design, our model can produce results immediately once the first frame generated..
Despite being few-step autoregressive design, AR AR-Drag still delivers the best visual quality. Specifically, it achieves the lowest FID and FVD, as well as the highest Aesthetic Quality, re- flecting superior visual fidelity and temporal coherence. In terms of motion control metrics, model attains the highest motion smoothness and consistency, highlighting its strength in precise and stable motion control. This contributes RL post training, which incentivizes the model's
7

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(a) Prompt: brown dog, shaking head, sits on light-coloreen sefa in
(b) Prompt: motion along trajectories
Tora
Self- Forcing
MegicMotion
Drag
(c) Prompt: the person takes off their clothes with the har ds and garments moving along the trajectory.
(d) Prompt: the clothes the character are blown by the wind, fallowing the trojectory.
Self Self-Forcing
Orog
Qualitative comparisons with Tora and Self-Forcing across different prompts, data do- mains, and resolutions, demonstrating the superior fidelity and controllability of our method.
Figure 3:
ability to follow motion guidance, enabling more flexible and robust controllability. Remarkably, AR-Drag even outperforms the SB MagicMotion, particularly on conton-conntol. MagicMotion does not utilize RL training, which limits its ability to achieve fine-grained, highly flexible control.
Self-Forcing baseline also adopts a few-step AR design, but requires 0.95s-more more than twice our latency-since it denoises three frames simultaneously. Moreover, AR-Drag outperforms Self- Forcing in both visual quality and motion control. These results demonstrate the effectiveness of our RL post-training and Self-Rollout for real-time motion-controlllable video generation.
Qualitative comparisons. We conduct qualitative comparisons with three competitive baselines, Tora, MagicMotion and Self-Foreing. As shown in Fig, 3 we evaluate across different prompts, ranging from specific actions such as head shaking and taking off clothes, to more general motions such following trajectory. We further compare performance and real-world data (b), as well as across different resolutions. Since Tora only supports a fixed resolution, the resolution-based comparison in (c) and (d) is conducted only against Self-Forcing. For clarity, we visualize the entire trajectory across frames in blue and highlight the control signal of the current frame in red. The reference image is provided for the first frame. Since the same negative prompt is applied to all videos, only the positive prompt is shown.
both synthetic data (a), (c), (d)
As illustrated in Fig. Ba&b), Tora and MagicMotion struggle to maintain consistency with the control signals. Self- Self-Forcing achieves partial controllability but suffers from deformation and quality degradation. In contrast, our method delivers superior fidelity and control alignment. Furthermore, as shown in Fig. (&dd), Self-Forcing exhibits substantial detail loss particularly in fine structures such as fingers and hair strands and suffers from increased color saturation in (c), whereas our method consistently preserves high-quality details and maintains faithful motion control.
4.2 ABLATION STUDIES
In Tab. 2 present the ablations on key training strategies.
w/o RL. Removing reinforcement learning leads to a noticeable drop both quality and motion- related metrics, highlighting the importance of RL in enhancing fidelity and motion controllability.
8

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Table Ablation study on key training strategies. "w/o RL' denotes removing the RL post-training. 'Initial model' refers to Wan2.1-1.3B-122 prior to adaptation. 'Teacher model' is the fine-tuned multi-step bidirectional model. 'w/o Self-Rollout' denotes training without the Self-Rollout design.
Aesthetic Quality
Motion
Motion
Method
Latency (8) 4
FID 4
FVD +
Smoothness
Consistency
AR-Drag w/o RL
0.44
28.98
187.49
4.07
0.9948
4.37
0.44
31.65
210.35
3.92
0,9926
4.12
Initial model
45.72
35.94
303.16
3.84
0,9915
3.22
Teacher model
45.64
29.38
151.46
4.15
0,9941
4.36
w/o Self-Rollout
0.44
38.13
353.75
3.38
0.9904
4.02
AR Drag
Teacher Model
w/a RL
w/o Self loflRRlloutt
Figure 4: Ablation on key training strategies. Prompt: movement following the trajectory.
Initial model. The initial 1-1..1-1. model performs worse than our base model (w/o RL) on video quality and have high latency, demonstrating that motion fine-tuning and real-time post-training strategies provide a strong foundation for RL training.
Teacher model. The teacher model, fine-tuned bidirectional multi-step baseline, achieves strong performance but suffers from high latency. While it represents the upper bound of DMD-based method, AR-Dr Drag achieves comparable even better results in FID, Aesthetic Quality, Motion Smoothness, and Motion Consistency, confirming the effectiveness of our RL approach.
Removing the Self-Rollout design leads to severe quality degradation, under- scoring its necessity for maintaining the Markov property and mitigating the train-test mismatch in autoregressive generation.
w/o Self-Rollout.
Visualization. Since the initial model performs significantly worse, we exclude it from the com- parison. As shown in Fig. due to the absence the feet in the reference image, both the teacher model and the model without RL fail to generate clear foot details, reflecting limited generalization. In contrast, our RL-baaed method encourages exploration, enhancing the model's 's generalization ca- pability. Additionally, the model w/o RL exhibits increased color saturation, while the model with- out Self-Rollout suffers from severe image artifacts and quality degradation, caused by the train test discrepancy and the disruption of the Markov property.
Visualization on diverse motion. We show qualitative results of our model conditioned on different motion trajectories in Fig. 5] The results demonstrate that method can accurately follow diverse motion commands, while preserving visual quality, and temporal consistency across frames.
5 DISCUSSION
Difference between Self-Rollout and Self-Forcing. Applying GRPO to AR VDMs requires the base model follow the Markov Decision Chain. However, standard AR VDMs exhibit train-

---

Figure 5: Visualization diverse motion. Prompt: movement following the trajectory.
ing-inference gap-training conditions on ground truth while inference conditions on generated frames-breaking the Markov property and preventing direct GRPO training. Self-Forcing partially reduces this gap by using self-generated frames as KV cache (Alg. 2 but it skips remaining de- noising steps and thus still violates the MDP. Our Self-Rollout enforces the full denoising rollout for every frame, restoring the proper Markov structure. This simple but critical correction makes RI. training valid and leads to clear performance gains, as shown Table 1, Figure 3, and the 'w/o Self-Rollou ablations in Table and Figure 4. More details can be found in Appendix A.
Importance of selective stochastic sampling. In bidirectional VDMs, frames are denoised jointly, so the decision-chain length equals the number of denoising steps. In AR VDMs, however, frames are denoised sequentially, making chain length scale with denoising steps frame count, leading to extremely long horizons. This causes return variance to explode and gradient estimates to be- come unusably noisy, making direct GRPO (or any policy-gradice method) practically infcasible. Our selective stochasticity sampling provides controlled exploration each step without triggering variance explosion, enabling stable and sample-efficient GRPO training for AR video generation.
CONCLUSION
We present AR-Drag, the first RL-enhanced few-step autoregressive video diffusion model for real-time motion-controllable image-to-video generation. By combining selective stochasticity, and trajectory-based reward model, our approach effectively addresses the challenges of quality degra- dation, motion artifacts, and complex control spaces few-step AR video generation. Extensive experiments demonstrate that AR-Drag achieves high visual fidelity, precise motion alignment, and significantly lower latency compared with state-of-the-ar motion- controlllnt VDMs, while maintaining compact model size of only 1.3B parameters.
10

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ACKNOWLEDGEMENTS
This research is supported by the National Research Foundation, Singapore under its Al Singapore Programme (AISG Award No: AISG3-RP-2002-053 the Ministry of Education, Singapore, under its Tier-1 Academic Research Fund (No. 24-SIS-SMU- 040). This research is also supported by National Research Foundation, Singapore, NRE-NRFIIO- 2024-0004.
This project is also partially supported by
ETHICS STATEMENT
This work presents a method for real-time controllable video generation. Our experiments are con- ducted de-identified datasets that do not contain personally identifiable information. The study is intended solely for scientific research, and we adhere to the ICLR Code of Ethics regarding fairness, integrity, and responsible use of data and models.
REPRODUCIBILITY STATEMENT
We provide detailed implementation settings, including model architecture, training objectives, op- timization strategies, and hyperparameters in the main text and Appendix. The code, configuration files, and instructions for reproducing the main experiments are available in Supplementary Materi- als to facilitate verification and further research.
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A LIMITATION AND FUTURE WORK
Our generative model is trained on data that follows physical plausibility, and reward model is also designed to evaluate motion based on physical principles. Therefore, if user intentionally pro- vides highly exaggerated physically impossible control signals, the model may not strictly follow such inputs because they fall outside the distribution it is trained and rewarded to respect. Handling deliberately non-physical cartoon-like motion is interesting direction for future work, and believe extending controllability beyond physically plausible dynamics is a valuable avenue for ex- ploration. Although our method already achieves real-time motion-controllabb video generation, future work could further reduce wall-clock latency by trading additional parallel compute for faster inference, e.g., via parallel sampling (Zhu al. 2025a Wang et al. 2025c).
B MORE DETAILS ABOUT ARCHITECTURE
To better illustrates the difference between Self-Rollout and Self-Forcing, we provide the detailed training process in Alg. and Alg. 2 respectively. As shown in Line 7 of Alg. 2 Self-Forcing randomly selects f, from denoising schedule and use the output at this step to compute loss (Line 9), ensuring that all denoising steps could be optimized. However, it directly treats the output at this intermediate denoising step as the final generated clean frame (Line 8), and uses it to update the KV cache (Line 10). This skips the remaining denoising steps from N, which breaks the MDP defined in Eq. 9 And the incorrect KV cache subsequently affects future generation. To address this issue, our Relf-Rollout continues the denoising process all the way step N. enforcing the full This complete rollout is implemented in Lines 18-28 of Alg. [
MDP transition defined in Eq. 9
In addition, we also provide the pseudo-code of Self-Rollout and Self-Forcing in Listing and 2.
C MORE EXPERIMENTAL SETTINGS
C.I DATA CURATION
We construct our training corpus by combining both real and synthetic videos to cover diverse mo- tion patterns. For the real vidcos, we directly collect footage from real-world sources. For the syn- thetic videos, use Wan2. 1-14-1222 to generate videos containing wide range of motion types but without control signals. In total, we gather approximately 10,000 videos. We then generate trajectory -based control signals using automatic detector, followed by manual filtering to remove videos containing sensitive content, low-quality samples, or incorrect trajectories. For challenging scenarios, such as severe occlusion fast motion, curate high-quality subset of approximately 3,000 videos, all of which are fully annotated by human annotators. Control signals include motion trajectories, prompts, and reference images. For motion trajectories, to better simulate actual user interactions, we represent each point as bright spot with intensity ranging from 0 to I rather than single isolated coordinate, mimicking the user's touch force on each frame.
For prompts, we provide both negative and positive prompts. The negative prompt is shared across all videos and follows the template: Negative Prompt Template
Overly vivid colors, overexposed, static, blurry details, subtitles, style, artwork, frame, still, overall grayish, worst quality, low quality, JPEG compression artifacts, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, malformed limbs, fused fingers, motionless frame, cluttered background, three legs, many people in the background, walking upside down.
For positive prompts, we include either general motions along trajectories or specific actions to guide the desired video content.
To handle videos of varying resolutions, we define a set of predefined "bucket sizes" and resize each input video to its nearest bucket. The buckets include resolutions such 480x368, 400x400,
16

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Algorithm Self-Rollout Training
Require: Denoising schedule (to, .to.,,,..t........ tN), Number of frames M + 1, model Go.
(Co: c1.-.., CN)
1: loop
2: Initialize model output X, +- 0. KVeache KV +-
Sample s
4: for m 0 to M do
5: Initialize Xm,0 NOO, for to do
6:
7:
n = then
Ensure all denoising steps could be optimized
8:
Enable gradient computation Cm, Xm.N + Gg(Xm. Cm. KV)
9:
10:
X appeen((mm.))
11:
else
12:
Disable gradient comoutation
13:
Xm,NN + Gø(X ta, Cm, KV)
Sample N(,
15:
km f V (&m,NN (x-1)
end if end for
17:
Xm,s+1 f- V,, (,,4+1))
19:
for 1 N do
n then
Enforce the MDP defined in Eq.
21:
Xm.N t- Go (Xm, tas Cm KV)
22:
kvm + GB (8m,,, KV)
Update KV cache with the right generation
23:
KV KVappend(kvvm)
24:
else
25:
Nm,N Go(XXmms bas Cm, KV)
26:
Sample e ~ NO,1)
Xm, v N,
28:
end if
end for
30: end for
Update on X9 end loop
32:
368x480, 640x368, and 368x640. This strategy ensures consistent input dimensions while preserv- ing aspect ratios as much as possible.
Table 3: Parameter analysis.
Aesthetic Quality
Motion Smoothness
Motion Consistency
Method
Latency (s) 4
FID 4
FVD 4
AR-Drag
0.44
28.98
187.49
4.07
0.9948
4.37
chunk size
3
0.94
27.47
179.23
4.09
0.9945
4.37
0.44
28.96
188.08
4.07
0.9946
4.34
cache size
25
0.46
28.99
185.31
4.05
0.9948
4.39
C.2 IMPLEMENTATION DETAILS
We implement our base model using Wan2.1-1.3B-12% (Wan et al. 2025). employing a 3-step dif- fusion process with N 3, and timesteps to 1000, t1 755, to size as 1, cache size as For distillation post training, we set DMD loss weight as 1, generator loss weight 0.1, discriminator loss 0.05.
522, to 0. We set chunk
17

---

Algorithm 2 Self-Forcing Training
Require: Denoising schedule (to, 81, ....... IN Number of frames M + model G3, control signals
(Co: ci...., CN)
1: loop
2: Initialize model output X, +- D. KVeache KV +-
Sample s
4: for m to M do
5: Initialize Xm,0 NN(0,, for to do
6:
7:
n = then
P. One-step collapse from s to N, which skip steps MDP Xm,N +- Go(Xm,o bas Cm, KV)
8:
9:
xp.apend((3m.N
10:
kvm f- G& (Xm,N, KV)
Update KV cache with the collapsed generation
11:
KVappeendkvy,.
12:
else
13:
Nm,NN + Go(Xxmns ban Cm, KV)
Sample N(0,1)
15:
8m. t- V (8m, 6,
end if
17:
end for
end for
19:
Update 0 on X
end loop
(e) Smoothed Molion Consistency Reward
Smoothed Aasthetic
4.375
4.06
4,300
Volle
4.00
4,275
3.90
4.250
3.96
4.255
4,200
3.92
100
200
300
400
500
600
100
200
300
400
500
600
Stop
Step
Figure 6: Smoothed Reward Curves for Motion Consistency and Aesthetic Quality
D MORE ANALYSIS
D. PARAMETER ANALYSIS
We conduct parameter analysis, shown in Tab.
Chunk size. Typical AR VDMs operate in chunk-wise manner, applying bidirectional attention within each chunk and causal attention across chunks. During inference, chunk-wise AR VDMs denoise all frames chunk simultaneously, which introduces some latency. contrast, our ap- proach adopts Trame-wise strategy, denoising one latent at time. While this increases the potential for error accumulation, the combination of Self-Rollout and RL post-training allows us to achieve comparable performance even with chunk size of 3.
Cache size. We set the KV cache size to 7 in our experiments. During inference, when the cache exceeds this length, the earliest frame is removed to maintain the fixed size. We observe that varying the cache size has little impact on the final performance, indicating that our method robust to different cache lengths.
18

---

def solf_ (model, x_g,, cond_list, schedule):
schedule 1t_0, t_,,
t_N], len (schedule)
cond_list [e_0, o_l, C_M]
M 1en (ccod_ooodsssl
X_theta
col lect supervised sed clean predictions
KV
KV cache
Sample random supervised prefix length
randem, randint (0, N)
Unif(o, N)
11
for m range (M+1):
12
c_m cond_list [m]
13
x = Berch.randd_iaee (x_
# fm, 0} (O,
===Phase 1: Supervised prefix (0 to 9) for n range 1):
16
# n 0,2,...,3
17
1f n == 8:
18
# Last supervised step: gradient flows
torch.enable_graat
20
predict clean frame
x hat model (x, schedule [n], e.m, XV)
22
X a_theta (x _a___a___
else:
torch.no_grad.
25
x_hat_N model (x, schedule c_m, XV)
26
epsilon = randd_aan_kik
27
x reverse_step(__hat___
epsilon, schedule[nt1]) epsilon, schedule(st1])
reverse (t__stteep__attt________
===Phase 2: Self-generated rollout (s+1 to N) torch.no_grad()
30
31
for n in range + 1, N 1): # Final step: update KV cache if -- N:
32
x_hat_N model (x, schedule c_m, KV) I extract KV from clean frame kv_m = model.get_kv (x_ KV)
35
36
KV (avappdnd(kv_)))
else:
39
x_hat_N model (x, schedule [n], e_m, XV)
40
epsilon = rarch (x_ (x__at_) x reverse_stepp (x_ hat_N, epsilon, schedule(nt1)
41
Update model using loss collected clean frames loss loss_func, (X_theta, x_gt)
44
45
boss.backwardd
optimizer. step
optimizer ()
Listing 1: Pseudo code for Self-Rollout
D.2 VISUALIZATION OF REWARD CURVES.
Fig. illustrates the training dynamics our two reward signals: Smoothed Motion Consistency Reward and Smoothed Aesthetic Quality Reward. Both curves show a clear upward trend as training progresses, reflecting the model's improving ability to maintain coherent motion and generate vi- sually appealing outputs. The motion consistency reward rises steadily, indicating better alignment with the target trajectories, while the aesthetic reward demonstrates rapid gains in the early stages followed by slower convergence, suggesting progressive refinement in visual quality. Together, these smoothed reward curves highlight the effectiveness of our reinforcement learning design in balancing motion control and perceptual quality.
19

---

def solf_ (model, x_g,, cond_list, schedule):
schedule 1t_0, t_,,
t_N], len (schedule)
cond_list [c_0, o_l, C_M]
M 1en (cond_ 1ist)
X_theta
col lect supervised sed clean predictions
KV
KV cache
Sample random supervised prefix length
randem, randint (0, N)
Unif(o, N)
11
for m range (M+1):
12
c_m cond_list [m]
13
x = Barch,randd_iaee (x)
# fm, 0} (O,
===Phase 1: Supervised prefix (0 to 9) for n range 1):
16
# n 0,2,...,3
17
1f n == 8:
18
# Last supervised step: gradient flows
torsh.enable_graat
20
predict clean frame
x hat N = model (x, schedule [n], c.m, XV) x append (x ____at__)
22
# extract KV from collapsed generation
kv_m model.get_kv ke (x_hat_,, KV)
25
KV aV.append (kv_m)
26
clse:
27
()
x_hat_N = model (x, schedule[n), c_m, KV)
epsilon (t_ee_stteeee_ta___eeeeaee hatha__ epsilon, scaedule
torch
30
x reverse_ stree_step__ee
31
x = reverse_step (x hat epsilon, schedule(st1])
32
Update model using DMD loss on collected clean franes loss = loss_ (X_theta, x_gt)
35
36
loss boss.backward
optimizer. step
optimizer.zero_gttee
Listing 2: Pseudo code for Self-Forcing.
E LLM USAGE STATEMENT
ChatGPT was employed solely for minor editorial assistance, such as improving grammar and read- ability. The research ideas, methodology, experiments, and analysis were entirely developed and conducted by the authors without the use of L.MMs.
20


Reviews:
[{"review_id": "8fgR5rLhMe", "reviewer": "Reviewer_7uAx", "summary": "This paper presents AR-Drag, a real-time, motion-controllable autoregressive (AR) video diffusion model optimized for I2V generation. It considers two challenges in AR video diffusion: the quality degradation and motion artifacts due to error accumulation and limited support for complex motion control. A two-stage training pipeline is proposed. The first stage is the base model training, including distilling the model into a few-step AR model, coupled with self-rollout, a training strategy that aligns training with inference. The second stage is reinforcement learning enhancement, which use GRPO to optimize the motion fidelity and visual quality via a trajectory-based reward.", "questions": "Please see the weaknesses.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. The model has the real-time inference capability, orders of magnitude faster than bidirectional models, and achieved low FID and FVD score.\n\n2. This work applies GRPO to AR video diffusion, introducing a trajectory-based reward for quality improvement.The", "weaknesses": "1. The novelty is quite limited, as the core components, like distilling, self-rollout, GRPO for video generation, have been individually explored in prior works. The proposed self-rollout only improves from self-forcing with minor modifications. The selective stochasticity adopted by AR-Drag that only chooses one denoising step instead of the whole stochastical trajectory is also a simple improvement.\n\n2. The comparison in Table 1 is not that fair. AR-Drag uses the Wan backbone, which is much better than the backbone adopted in some competitor methods, line DrageNUWA, Tora. For Self-Forcing, it is not specifically designed for I2V (it’s backbone is T2V).\n\n3. I didn’t find the detailed description of the conditioning on the previous frames (i.e. how the AR works). Only a figure is presented.", "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": "Sb2mg0RkWi", "reviewer": "Reviewer_4Dc7", "summary": "The authors propose a real-time video generator using autoregressive diffusion model, named AR-Drag. To achieve it, they first finetune a I2V model to achieve motion control. Then the model adopts post-training via reinforcement learning with a trajectory-based reward model. It achieves the state-of-the-art performance using only 1.3B parameters.", "questions": "Please see the weaknesses.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Real-time video generation is a crucial topic in the video generation community. AR-Drag proposes a valid solution to it.\n2. The latency analysis shows AR-Drag is close to real-time generation. The quantitative metrics demonstrates the method achieves the SOTA performance in trajectory controlled video generation.\n3. The method incorporates a GRPO post-training process that further improves the performance.", "weaknesses": "1. An important baseline is missing. Please compare AR-Drag to MagicMotion.\n2. Can you compare AR-Drag with Longlive[2], which is alose a real-time video generation with 1.3B parameters.", "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": "r5U5aIffVb", "reviewer": "Reviewer_Zmb3", "summary": "AR-Drag is a novel real-time video generation system that addresses the latency limitations of traditional diffusion models by introducing the first reinforcement learning-enhanced autoregressive approach for image-to-video generation with motion control. The method fine-tunes a base model to support motion control and then improves it through reinforcement learning with a trajectory-based reward model, utilizing a Self-Rollout mechanism to preserve the Markov property and selective stochasticity to accelerate training. With only 1.3B parameters, AR-Drag achieves high visual quality and precise motion alignment while significantly reducing generation latency compared to existing state-of-the-art motion-controllable video diffusion models, overcoming the quality degradation and motion artifacts common in few-step generation approaches.", "questions": "Please refer to weaknesses.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. This paper introduces the first RL-enhanced autoregressive video diffusion model that enables real-time motion-controllable generation with significantly reduced latency compared to existing bidirectional approaches.\n2. This paper proposes a Self-Rollout mechanism that preserves the Markov property while selectively introducing stochasticity in denoising steps, enabling efficient reinforcement learning training for few-step video generation.\n3. This paper achieves high visual fidelity and precise motion alignment with only 1.3B parameters, overcoming the quality degradation and motion artifacts that typically plague few-step autoregressive video generation methods.", "weaknesses": "1. Compared to the pretrained model used, the video quality provided in the supplementary materials of this paper shows significant degradation with very obvious artifacts. For example, there are very obvious artifacts in the cat's pants in the first video.\n2. The authors only provide very few videos in the supplementary materials, and none of these videos are annotated with motion control.\n3. The authors do not discuss the limitations of this method in the paper. Please ask the authors to supplement this in the rebuttal.", "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": "MTvfPXzSzP", "reviewer": "Reviewer_RqLj", "summary": "This paper proposes AR-Drag, a trajectory-based real-time video diffusion model. AR-Drag is built on top of WAN-2.1-1.3B, using motion keypoints to define the motion trajectory. To further improve motion quality and latencies, the paper introduces two designs: i) distilling bi-directional attention in the base model into a few-step autoregressive model with self-rollout, similar to self-forcing but applying DMD on the randomly sampled n-th denoised frame; ii) the model generates history frames, leading to a stochastic MDP rollout that optimises GRPO with motion controllability and visual realism rewards. These improvements result in significantly reduced latencies and enhanced motion and generation quality compared to previous methods.", "questions": "See the weaknesses.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "The paper is well-written and has a clean, straightforward design. The modification of self-forcing to introduce selective stochasticity on the sampled demonised step n is a smart design.  The inclusion of the source code is highly appreciated.", "weaknesses": "- The attached website is rushed and incomplete. It lacks examples of generations from the baselines and without visualisation of the conditioning motion trajectories, and it includes wrong input prompts. This makes it difficult for the reviewer to appreciate the generation qualities, especially for a videogen model. These visualisations would be crucial for supporting the experiment.\n- Directly optimising test metrics within model training is slightly cheating compared to other baselines. While I understand GRPO is an essential part of the design, I feel like highlighting FID/FVD alone would be more fair. Additionally, having these metrics purely as an ablation for the RL component, as shown in Table 2, where the performance is also very good, seems sufficient.\n- The training data collected by this paper is also important. The authors should provide more details about the collected data, including the source of the videos, how they are filtered, and the total data size of the videos. I am wondering whether the authors also consider open-sourcing the collected data?\n- Considering the core design of the self-rollout is very similar to self-forcing, I would suggest adding a section on a side-by-side comparison of the implementation levels (like pseudo-code) to highlight the implementation differences. This would provide a clearer understanding of the method design.", "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)