ConsisDrive: Identity-Preserving Driving World Models for Video Generation by Instance Mask
Authors: Zhuoran Yang
Yanyong Zhang
Venue: ICLR 2026 Poster (2026)
Keywords: Temporal Consistency
Video Generation
Primary area: generative models
TL;DR: We present a identity-preserving world model that generates realistic multi-view driving videos with superior fine-grained temporal consistency.

Abstract: Autonomous driving relies on robust models trained on large-scale, high-quality multi-view driving videos. 
Although world models provide a cost-effective solution for generating realistic driving data, they often suffer from identity drift, where the same object changes its appearance or category across frames due to the absence of instance-level temporal constraints. 
We introduce **ConsisDrive**, an identity-preserving driving world model designed to enforce temporal consistency at the instance level. 
Our framework incorporates two key components: (1) Instance-Masked Attention, which applies instance identity masks and trajectory masks within attention blocks 
to ensure that visual tokens interact only with their corresponding instance features across spatial and temporal dimensions, 
thereby preserving object identity consistency; 
and (2) Instance-Masked Loss, which adaptively emphasizes foreground regions with probabilistic instance masking, reducing background noise while maintaining overall scene fidelity. 
By integrating these mechanisms, ConsisDrive achieves state-of-the-art driving video generation quality and demonstrates significant improvements in downstream autonomous driving tasks on the nuScenes dataset.

Published as a conference paper at ICLR 2026
IDENTITY-PRESERVNG DRIVING WORLD MODELS FOR VIDEO GENERATION BY IN- STANCE MASK
CONSISDRIVE:
Zhuoran Yang, Yanyong Zhang University of Science and Technology of China shanpoyangjmmiil. wst.. wus.t.edu....................... en, yanyonggzustcc.edu.co
ABSTRACT
Autonomous driving relies robust models trained on large-scale, high-quality multi-view driving videos. Although world models provide cost-effective so- lution for generating realistic driving data, they often suffer from identity drift, where the same object changes its appearance or category across frames due the absence of instance-level temporal constraints. We introduce ConsisDrive, an pheetity-preserving driving world model designed to enforce temporal consis- tency the instance level. Our framework incorporates two key components: (1) Instance-Masker Attention, which applies instance identity masks and tra- jectory masks within attention blocks to ensure that visual tokens interact only with their corresponding instance features across spatial and temporal dimen- sions, thereby preserving object identity consistency; and (2) Instance-Masked Loss, which adaptively emphasizes foreground regions with probabilistic instance masking, reducing background noise while maintaining overall scene fidelity. By integrating these mechanisms, ConsisDrive achieves teee----thh-rtttt driving video generation quality and demonstrates significant improvements in downstream au- tonomous driving tasks the nuScenes dataset. Our project page is here"
INTRODUCTION
Autonomous driving has attracted extensive attention from both academia and industry over the past decades Shi al. (2016); Zheng et al. (2024a); [Chnn et al. (2024): Jiang et al. (2023). To achieve re- liable performance, autonomous systems rely on high-quality, large-scale multi-view driving videos with precise annotations, which are essential for training perception, tracking, and planning mod- els. However, collecting and labeling such real-world driving data is both costly and labor-intensive. Benefiting from the rapid advancements in generative video models et al. (2023); xi ei al. (202));
Zheng et al.) (2024b): HaCChen al. (2024); Wang et al. (2024a); Gao et al. (2024)) Zhou et al. (2024) Ho et al. (2022): Blatmannn et al. (2023); Hu (2024); Wang et al. (2023a) (022bb; Bar- Bar-Tal Tal al. 2024); Gupta al. (2024), driving world models Zhao et al. (2024): Wen et al. (2024): Jia et al. (2023): Wang et al. (2023c); Gao et al. (0024)) have emerged as a promising alternative.
These models can synthesize diverse and realistic driving scenarios at scale, significantly reducing the demand for costly data collection and annotation.
Instance identity preservation across frames is critical for generating realistic driving videos, as directly affects video quality Unterthiner et al. (2018) and determines their applicability in down- stream autonomous driving tasks. For example, multi-object tracking Wang al.) (2023b) and per- ception tasks Wang al. (223b)) require temporally stable instance appearances to ensure reliable temporal context understanding. Similarly, planning Hu et al. (2023) relies on temporally coherent trajectories of surrounding agents to support accurate motion forecasting. These requirements ne- cessitate that the world model consistently maintains instance identitics such as category, color, and shape- -capssscross consecutive frames, ensuring continuity in both appearance and behavior of dy- namic objects. From a broader perspective, the ability to preserve instance identity essential for
"Corresponding Author

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Published as a conference paper at ICLR 2026
world models to effectively capture the underlying dynamics of real-world environments. Techni- cally, enforcing strong temporal consistency improves the reliability of autonomous driving models trained on synthetic data, ultimately enhancing their generalization real-world scenarios.
DriveDreamer2
Ours
MagicDriv--V2
Ours
Frame
(4) Category Shift
(b) Color Shift & Foreground Dilution
Figure 1: Limitations of Prior Works in Instance Identity Preservation Across Frames. (a) Category Shift: In DriveDreamer2 Zhao et al.] (2024), the bus gradually turns into a truck. indi- cating a failure to preserve semantic identity over time. (b) Color Shift: In MagieDrive- V2 [aao eet al. (2024)), the car's color changes inconsistently across frames, violating temporal appearance consistency. (b) Foreground Dilution: In MagicDrive- V2 Gao et al. (2024b), scene-level supervi- sion dilutes supervision over critical foreground regions, breaking temporal identity consistency for small instances like pedestrians. In contrast, our method explicitly enforces instance-level temporal constraints, maintaining consistency across frames and effectively addressing these issues.
However, existing liffusiio-bbsed world models frequently suffer from identity drift, where the same object changes its appearance or even category across frames (c.g., red car becoming black, or a bus turning into a truck), as shown in Fig. [ Such identity inconsistency severely degrades video realism and limits the applicability of generated data for downstream driving tasks. We identify three major root causes of this problem. First, the absence of explicit instance identity conditions prevents the model from anchoring consistent identities over long horizons. For example, DriveDreamer2) Zhao al. (024)) does not incorporate instance-speciffe conditions such category, leading to noticeable semantic shifts, as illustrated in Fig. [a). This highlights the necessity of injecting explicit instance identity signals into the generation process. Second, the attention mechanism of current diffusion transformers is not instance-aware. For instance, FLUX's MMDiT Labs (2024) computes 3D full attention across all visual tokens from different instances. This makes the attention mechanism unreliable and causes information leakage between different instances. Models such as MagicDrive- V2 Gao et al. (2024b) integrate temporal attention layers enhance inter-frame global coherence, they lack fine-grained, instance-aware temporal alignment, suffering from identity inconsistencies such as color shifts, as shown Fig. bb). This underscores the need for instance-aware attention mechanisms. Third, existing training objectives Wen et al. (2024) apply uniform supervision over the entire frame, forcing the model to reconstruct both background pixels (c.g.. sky, buildings) and critical foreground regions with equal importance. Since background pixels dominate the scene, this supervision dilution prevents the model from focusing on fine-grained identity- Consequently, foreground temporal consistency is easily broken, especially for small objects, as shown in Fig. [b) in MagicDrive-V2 Gao et al. (2024b). This motivates the design of instance- aware training objectives that emphasize foreground regions.
features.
To address the above challenges, we propose ConsisDrive, an identity-preserving driving world model specifically designed to enforce instance-level temporal consistency. Our framework incor- porates instance awareness into both the attention mechanism and the training objective, guided by carefully constructed instance masks. particular, we introduce two core components: The Instance Masked Attention (IMA) module explicitly guides the model's attention towards each in- dividual instance, effectively preventing information leakage across multiple instances. Specifically, by constructing instance identity mask, we restrict visual tokens to attend only to the identity em- beddings of their corresponding instances. This preserves the identity of the instance across long sequences, effectively mitigating identity drift (e.g., preventing bus from gradually being inter- preted as a truck). What's 's more, by constructing instance trajectory mask, we ensure that tokens of the same object across frames exclusively attend each other, while interactions across different
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instances are strictly blocked. This design allows the model to reliably propagate appearance fea- tures such as color and texture along the trajectory of each object, thereby avoiding cross-instance information leakage and ensuring consistent instance-level visual identity across time. Second, the Instance Masked Loss (IML.) addresses the supervision dilution problem caused by uniform loss computation across entire frames. IMI. employs instance masks emphasize supervision fore- ground regions during training. A probabilistic dynamic masking strategy is further introduced, which adaptively balances between foreground-focuue focussed loss and global reconstruction loss. This design ensures that foreground consistency is enforced without sacrificing overall scene fidelity, allowing the model to capture both fine iine-grained identity details and natural background appearance.
Through the joint design of Instance Masked Attention module and Instance Masked Loss Supervi- sion, ConsisDrive significantly mitigates identity drift and achieves temporally consistent video gen- eration for driving scenarios. Our approach achieves state-of-the-arr performance in both video gen- eration quality and downstream autonomous driving task validation, outperforming previous works Gao et al. 2024b); Zhao et al. 22024) Li et al. (2023) Wen et al. (2024). Our contributions are as follows.
We propose the Instance-Masked Attention module, which explicitly directs the model's attention to each individual instance. By incorporating both an instance identity mask and a trajectory mask, the module constrains visual tokens to attend exclusively to tokens of their corresponding instances across spatial and temporal dimensions. This design effec- tively enforces instance-level temporal consistency while preventing information leakage between different instances.
We design Instance-Masked Loss Supervision, a probabilistic fnstance-focused training objective that employs instance masks emphasize supervision on foreground regions.
Our model achieves SOTA video generation quality with high FID and FVD the nuScenes benchmark, surpassing previous methods. For autonomous driving applications, the generated videos are validated on downstream perception, tracking, and planning tasks, with performance competitive to real-world sensor data.
2 RELATED WORKS
Street- View Generation. Street-view generation methods typically use 2D layouts like BEV maps, 2D bounding boxes, and semantic segmentation. BEVGen Swerdlow et al. 2023) encodes semantic data in BEV layouts, while BEVControl Yang et al. (2023) uses two-stage pipeline for multi- view urban scenes, ensuring cross-view consistency. However, projecting 3D information into 2D layouts loses geometric details, causing temporal inconsistencies in videos. To address this, we use 3D bounding boxes to maintain geometric fidelity. Unlike DrivingDiffusiin [i al. (2023). which relies on complex multi-stage pipeline, our method simplifies the process with an efficient, end-to-end framework, ensuring temporal coherence and computational efficiency.
Minulation-to-Real Visual Translation. Recent advances in synthetic data for real-world visual tasks have shown significant progress. GAN-based translation Guo et al. (2020) and domain ran- domization Tobin et al. (2017) bridge synthetic and real-world data distributions, while datasets like Synthia Ros et al. (2016) and Virtual ArTti[Cabon et al. (2020) provide scalable benchmarks for se- mantic segmentation and autonomous driving. Adversarial training (hrivvattaaa et al. (2017): /Zhang ct al. (2018) reduces distribution gaps, and human motion representation learning Guo et al. (2022)
highlights synthetic data's 's utility in video understanding. Unlike these methods, extract proxy data like 3D bounding boxes and road maps from graphics systems, leveraging these conditions to generate more realistic and diverse videos.
METHOD
We introduce ConsisDrive, an identity-preserving driving world model specifically designed to en- force instance-level temporal consistency. Our framework incorporates instance awareness into both the 3D full attention mechanism (Sec. B.2) and the training objective (Sec.B.3) where carefully con- structed instance masks serve as structural priors to guide consistency across frames. The overall architecture of our model is illustrated in Fig.
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Figure 2: Overview. (a) Instance-Maske Attention, which explicitly directs the model's attention to each individual instance by incorporating both an instance identity mask and trajectory mask. (b) mnstance-Masked Loss Supervision, probabilistic instance-focused training objective that employs instance loss masks to emphasize supervision on foreground regions. (c) Instance Mask Construc- tion. Illustration of how the Instance Identity Mask, Instance Trajectory Mask, and Instance Loss Mask are constructed from 3D box projections.
3.1 OVERVIEW
Building on OpenSora V2.0 Peng al. (2025) we employ a Variational Auto-Ennoder (Video DC- AE) for video encoding, T5XXL Chung et al. (2024) and CLIP-Large Radford et al. (202T) for text encoding. MMDiT
Labs 2024) as the foundational model for the denoising process.
To achieve fine-grained control, we introduce a comprehensive set of control conditions. including bounding box projection, road maps, and scene descriptions, integrating them into the conditioned video generation process. Moreover, we introduce instance category label, instance size, and track- ing ID to extract instance identity conditions and incorporate them into the attention process, a key mechanism within our proposed Instance-Masked Attention module, detailed in Sec. 3.2
Given the need to handle multiple control elements, we adopt ControlNet Zhang et al. 20033) to inject control signals into the video generation process. Too integrate these control-aware represen- tations, we duplicate the first 19 base blocks of the double-stream MMDiT backbone as dedicated control blocks. Each control block fuses condition features with the corresponding outputs of the base blocks, thereby modulating the feature flow and ensuring that control signals are effectively incorporated throughout the generation pipeline.
3.2 DASTANCE-MASKE ATTENTION
The Instance-Masked Attention module, as illustrated in Fig (a), is designed to guide the model's attention towards each individual instance. By constructing instance masks from the bounding boxes of objects in Fig (c), we effectively prevent information leakage across multiple instances. We add our proposed learnable Eistance-Masked Attention module to handle the per-instance identity conditioning. Instance-Masker responding outputs of the copy blocks and modulates its features to enable instance-aware attention. We now describe the key operations within the Maaance-Masked Attention module in detail.
Attention module fuses the instance identity conditions with the cor-
The Instance-Masked Attention module, as illustrated in Fig. a), is designed to explicitly guide the model's attention towards each individual instance. Instance masks are constructed from object bounding boxes, as shown in Fig. c). to prevent information leakage across instances and ensure instance-level disentanglement. We introduce a learnable mnstance-Masked Attention mechanism, which injects per-instance identity conditions and fuses them with the corresponding outputs of the copy blocks. By modulating these features, the module enables instance-aware attention that
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preserves instance identity and ensures instance consistency across spatial and temporal dimensions. We now describe the key operations of the Mistance-Masker Attention module in detail.
3.2.1 INSTANCE IDENTITY CONDITION
For each instance i that appears in the video, we construct a global condition that integrates multiple factors of the instance, including its category, its unique tracking ID, and the size of its bounding box. Together, these attributes provide an informative representation of both semantic identity and geometric configuration, which is crucial for preserving the instance identity consistently across frames.
Concretely, we first apply Fourier mapping 7(-) to encode the tracking ID ID, and the bounding box size 8; = (dx, dys, d2z). At the same time, we employ the CLip-Large RRdford et al. (2021) text encoder r-)-) to extract semantic feature from the category label c.. These components concatenated and passed through a multilayer perceptron (MLP) to produce the instance global identity condition embedding:
9t MLP(( (ID)))
(1)
The complete set of embeddings for all instances in the video is then denoted as:
= {g J
3.2.2 INSTANCE-MASKED ATTENTION AND FUSE MECHANISM
We denote the m = T Teompres Hoompress X W compress visual tokens extracted from the copy MMDiT block as V. and the instance condition tokens G. We then apply 3D self-attention (SA) over the backbone features and the concatenated instance condition tokens [V, G] in the Instance- Masked Attention module, which can be formulated as:
V Smmak (V, G)).
We observed that standard self-attention, without masking, leads to information leakage across in- stances, such as the color of one instance bleeding into another. To address this problem, we con- struct mask matrix M E R (m0+)) (xo-=(((mmm (m++)) to determine the valid connections:
Token-to-Instane Indicator Function. Indicator function I(Vx) denotes the set of instance IDs whose projected bounding boxes Formally, each instance i is represented by 3D bounding box with corners Cj = {X where Xi,e E R3, Given camera parameters (K', R T²) at frame é, the corners are projected as
the spatial region of token uk, illustrated in Fig (c).
x = K' (R'X,, + TT), x
( (
(3)
with the convex hull of (x(,) forming the polygon Pf Rasterization yields binary masks BM, (0, 1 JTXH>W We further apply trilinear interpolation map these masks into latent space, denoted as BM; For patch tokens p obtained from VAE compression, we define
[(vx = [tt, p) = {i (zz v), (t, x, y) = =1},
(4)
where 0g is a flattened visual token corresponding to (t.p).
Instance Identity Mask. For each visual token 2s and instance condition token 9,, the attention score is masked as
-oo if T(vx),
This mask ensures that each visual token can only attend to the identity condition token of the corre- sponding instance. This mechanism explicitly injects global identity features into the attention pro- cess, ensuring that each instance preserves its identity consistently across long temporal sequences. Meanwhile, strictly suppresses interactions between different instances, thereby preventing iden- tity leakage across objects.
Instance Trajectory Mask. For two visual tokens 2y and Vy, the attention score masked
Ma.j if ((xx) T(py) 0.
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This mask ensures that tokens of the same instance across frames can attend to another, while interactions across different instances are strictly prohibited. This design preserves temporal consis- tency by explicitly enabling the propagation of instance-specifee features across their trajectories.
Finally, the output of the Instance-Masked Attention is added back to the backbone representation via gated addition:
V = V tanh(w) V[: ml,
(5)
where wi is a learnable scalar parameter, initialized to zero, that adaptively controls the contribution of the Maskedd Attention module.
3.3 INSTANCE-MASKED LOSS
The goal of ConsisDrive is ensure that foreground objects in driving scenes remain consistent across all frames in the generated video. However, existing training objectives apply uniform su- pervision over the entire frame, forcing the model to reconstruct both background pixels (e.g., sky, buildings) and critical foreground regions with equal importance. Since background pixels dominate the scene, this supervision dilution introduces noise that interferes with model training, preventing the model from focusing on fine-grained identity-preserving preserving features. Consequently, foreground temporal consistency easily broken, especially for small objects.
To resolve this issue, we propose the Instance-Masked Loss, which focuses the supervision signal (b)(c). Specifically, we construct binary loss mask MLOss E {0, TTopp x Homp Wooe from the Instance-to-Tokee Indicator function [(v)). For each token 2h, the mask is defined as os (1x)) (((()))))) ensuring that only tokens covered by at least one instance are selected. The masked loss is then computed as:
on foreground objects, as illustrated in (b))).
Irmusk = MLsss L,
where L denotes the original denoising loss and @ indicates element-wise multiplication.
However, directly applying this masked loss to all training samples may cause the model to overfit foreground objects, which turn harms the generation quality of background regions, such as roads and high-definition maps. To alleviate this problem, we adopt probabilistic dynamic masking strategy. Specifically, with a probability p of a, the masked loss is applied:
Emesk = { Smaskk
p < a
Lc.
(6)
p 2 a
This stochastic scheme allows the model to concentrate on foreground consistency while still pre- serving the natural realism of background content.
EXPERIMENT
4.1
SETUPS
Datasets and Baselines. We train and evaluate our model on the nuScenes dataset Caesar et al. 2202)). To benchmark our approach. we compare it with state-of-the-art driving world models, including BEVControl Yang el al. (2023), DriveDiffusion Li et al. (2023), DriveDreamer2 Zhao
al. 2024), Panacea Wen et al. (2024) and MagicDrive-V22 Gao et al. (2024b).
Metrics. For realism assessment, we use FID et al. (2017) and WVDtetntrthinee et al. (2018) measure video quality. To evaluate the effectiveness of attribute binding and Instace Masked Loss Supervision, we measure the alignment between generated instances and their conditioned cate- gories and sizes, ensuring that both semantic and geometric structures are faithfully preserved. This evaluation is conducted through perception tasks, since accurate category recognition and size lo- calization are fundamental requirements for reliable perception. Hence, perception performance directly reflects the generation accuracy of object categories and spatial extents. Specifically, fol- lowing Panacea Wen et al. 2024), we adopt the video-based perception model StreamPETR Wang et al. (023b) and report metrics such the nuScenes Detection Score (NDS) and mean Average Precision (mAP). Among them, mAP directly measures the accuracy of object category detection, while NDS integrates category detection with localization, orientation, and other aspects provide

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holistic assessment of perception quality. To further evaluate the propagation of instance-specifer features across frames, we assess our model on the multi-object tracking (MOT) task in real-world autonomous driving scenarios. MOT explicitly measures the ability to maintain consistent object identities over time, using metrics like ID switches (IDS). We also adopt the StreamPETR model Wang et al. (2023b) the tracker and report standard MOT metrics, including AMOTA, AMOTP, and IDS, Although Bevfusion et al. (2033) has also been employed for perception evaluation Gao al. (2024c),
based on an image model, performs worse than the video-based StreamPETR, and lacks the ability to provide object tracking metrics. For these reasons, we choose StreamPETTR as our evaluation model.
Method
Munt-Siww
Mtll-Tome
FVD.
110)
BEVCnoo
Teggst
Hotr
24.35
13240
94.84
20.91
Unickcand Wib 11 pivce
15.30
$.34
Unee)radt 2201 2124
1122
102.22
Unibesnd Lipa 123T PIUR
10.51
6.12
CoackDerly
Table Visual and Temporal Fidelity: Comparison with SoTA methods on nuScenes validation set.
4.2
TRAINING DETAILS
Our method is implemented based on OpenSora V2.0 Peng et al. (2025). All training inputs were 16x256x448 and conducted on 64 A100 GPUs. Experimental results show that our method can stably generate over 200 frames.
4.3
MAIN RESULTS
4.3.1
QUANTITATIVE ANALYSIS
To verify the tine-grained temporal consisteney and high fidelity of our generated videos, we com- pare our approach with various state-of-the-ar driving world models. We generate training and validation data using the nuScenes dataset's labels as conditions.
Visual Realism and Temporal Fidelity. Our generated videos achieve superior temporal fidelity, reducing the FVD to 37.23. This improvement stems from the proposed Instance-Masked Atten- tion module, which preserves instance attributes across frames and thereby enhances instance- temporal consistency. In terms of visual quality, our method attains an FID of 3.88, as reported in Tab. 1 substantially outperforming both video-based approaches (e.g., DriveDreamer2)) and image- based solutions (e.g., BEVControl). These demonstrate that our generated images exhibit not only stronger temporal coherence but also significantly higher visual realism.
Meebed
Obail
S13
354
201
Firaces
233 200
723
433
3e MONDTe
Prensea
3F.1 (226%)
542
233
Connortiee (00M)
30
ComnDeiv (Oun)
45. (49.2%)
54.0442799)
Table 2: Comparison on perception tasks with Panacea. Training StreamPETR with synthetic data augmentation leads to significant performance improvements, highlighting the value of generated data for perception.
Methed
Real
Gen.
NDS1
Ondie
4kr
Panacea
(68 (68.055)
MrgieDrive-V2
ConstiDriv
(88.235)
Table 3: Comparison of perception task performance generated nuScenes (T+I)2V validation data. Evaluated with pre-trained StreamPETR Wang et al. (2023b), our model outperforms basclines without post-refinement, showing its ability to faithfully capture and bind instance attributes.

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Method
Real
AMOTAT
AMOTPI
IDS]
Oracle
0.289
1.419
687
DriveDremmer2
0.313
1.387
593 (-94)
InstaDéve
0.496
1.376
532 (-155)
CousisDrisc (Ouns)
0:498
1,350
525 (-162)
Table 4: Comparison involving data augmentation using synthetic data on multi object tracking.
Instance Attribute Binding. We evaluate instance-level temporal consistency by examining how well global instance attributes are bound the generated content. In autonomous driving, percep- tion tasks critically depend on precise category recognition and size localization, making them a natural measure of attribute binding quality. To this end, we assess the alignment between generated instances and their conditioned categories and sizes, which directly reflects the fidelity of instance attribute binding.
Data Augmentation Performance on Perception. As shown in Tab. 2 training StreamPETT exclu- sively on our generated dataset achieves mean Average Precision (mAP) of 31.5%. which cor- responds to 91.3% of the performance obtained by training solely on the real nuScenes dataset. Since mAP directly measures category detection accuracy, this result confirms that global instance attributes are reliably bound through our Instance Identity Mask (Sec. (.2). Moreover, the gener- ated dataset proves be not only a viable substitute for real data but also an effective standalone training resource for perception models. When we further re-train StreamPETR by augmenting real data with our generated videos, the perception model achieves a nuScenes Detection Score (NDS) of 54.6, representing 7.7-point improvement over training with real data alone. This demonstrates the substantial benefit of incorporating our generated data into the training pipeline.
Validation Performance Perception. Additionally, use the pre-trained StreamPETR model to evaluate the generated validation set of nuScenes. As reported in Tab. 3 our model achieves a rel- ative performance of 88.23% the NDS metric, highlighting strong alignment between generated content and conditioned instance categories and sizes.
Instance Attribute Propagation. Data Augmentation Performance on MOT. We further evaluate instance-level temporal consistency by measuring how effectively instance attributes are propagated across frames. In autonomous driving, this is naturally reflected by the multi-object tracking (MOT) task, since MOT requires tracking instances according to their consistent attributes over time. This makes MOT a strong indicator of attribute propagation quality. Concretely, we generate videos conditioned on labels from the nuScenes training set and use them to augment the training data and re-train the object tracking model StreamPETR WWang et al.] (2023b). Then we evaluate the real validation set using the re-trained StreamPETR. As shown in Tab the MOT model re-trained with our generated data shows significant improvements, achieving lower number identity switches (IDS 525) compared to the original pre- pre-trained StreamPETR. This demonstrates that the Instance Trajectory Mask in Distance-Masker Attention module effectively propagates and preserves instance attributes, ensuring reliable temporal consistency for downstream perception tasks.
4.3.2 QUALITATIVE ANALYSIS
We qualitatively compare ConsisDrive against state-of-the-art baselines by inspecting generated videos, as illustrated in Fig. I Please refer the project page for additional video result.
Instance Attribute Binding (Category). As shown in Fig. a DriveDreamer2 suffers from semantic drift, where the category of a car gradually changes into a truck. In contrast, Con- sisDrive preserves the semantic category consistently across all frames, demonstrating stronger instance-level temporal consistency. This improvement arises from the Instance Identity Mask in the Instance Masked Attention Module, which explicitly binds identity features to their correspond- ing instance identities.
Instance Attribute Propagation (Color). In Fig. [b). MagicDrive- V2 exhibits color inconsistency, where the color of the same car changes from black to red across frames. Our model maintains stable color and texture attributes throughout the video, indicating superior attribute propagation over time. This is enabled by the Instance Trajectory Mask in the Instance Masked Attention Module, which enforces cross-frame attention only within the same instance trajectory, preventing appearance drift.
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Foreground Emphasis. As shown in Fig. Tb), ConsisDrive enhances the fidelity of small and challenging objects (e.g., pedestrians). whereas Panacea produces blurred results for such instances. This improvement stems from the Instance Masked Loss Supervision. which prioritizes supervision on foreground regions even when the foreground spatial proportion is very small and background pixels dominate the scene, thereby preventing foreground signals from being diluted by background pixels.
Setingg Concithrnee
FVDJ
FID,
NDS!
[DS]
37.25
1.88
4133
525
IMA//emmit))
80.8 (+3.6))
5.29
31. 6:385)
(-210)
we (MAgooggy)))))
53.66 (-16.4))
(40.5%
80.0 (4)9))
1074 (+549)
we IMIL
40.19 (1296)
4.24 (10.26)
36.85 ( (435))
637 (+112)
Table Ablation study results in (T+I)2V scenarios on the nuScenes validation set.
w/o Identity Mask
Ours
w/o Trajectory Mask
Ours
Frame
(4) wro Identity Mask: Category Shift
(b) wio Trajectory Mask: Color Shift
Figure 3: Ablation study of the three key modules. (a) Removing the Identity Mask leads to incorrect instance category rendering, e.g., a traffic cone turns into a crouching pedestrian. (b) Removing the Trajectory Mask results in color shifts of the car.
4.4 ABLATION STUDY
We validate three key components in ConsisDrive through both qualitative and quantitative anal- yses, demonstrating their effectiveness and robustness, The qualitative comparison is presented in while quantitative results are reported in Tab. 5
Fig. 3
Instance Identity Mask. To assess the impact of the Instance Identity Mask within the Instance Masked Attention module, we conduct an ablation by removing the global instance identity condi- tioning (i.c., filling the instance identity mask with -oo). As shown in Tab. 5 the absence of global identity conditioning results a 3.83-point drop in NDS for the perception task. This confirms its critical role in binding category and size conditions to their corresponding instances, ensuring faithful instance attribute binding.
Instance Trajectory Mask. To evaluate the impact of the Instance Trajectory Mask, we perform an ablation by removing trajectory-basee attention (i.e., filling the trajectory mask with -oo). As reported in Tab. | removing this causes significant increase of 549 ID switches in the multi-object tracking task and 16. 25.44-ppmm 43--inn drop in FVD. These results underscore its importance in propagating and preserving instance attributes such as color consistently across frames.
Instance Masked Loss Supervision. To analyze the effect of Instance Masked Loss Supervision, we remove and retain only the standard denoising loss. As shown in Tab. 5 this results in a 4.53- point degradation in NDS, demonstrating that the module is crucial for emphasizing supervision on foreground regions. By preventing foreground signals from being overwhelmed when their spatial proportion is small, it ensures higher fidelity in generating small and visually challenging objects.
CONCLUSION
We propose ConsisDrive, an identity-preservii driving world model specially designed to enhance instance-level temporal consistency. Our approach introduces two key advancements: the Instance Masked Attention module, which explicitly directs the model's 's attention to each individual instance, and the Instance Masked Loss Supervision, which employs instance masks to emphasize supervision on foreground regions. By incorporating these instance-ate aware mechanisms, ConsisDrive achieves SOTA generation quality and significantly improves downstream autonomous driving tasks.

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ACKNOWLEDGMENTS
This work was supported by the Fundamental and Interdisciplinary Disciplines Breakthrough Plan of the Ministry of Education of China (No. JYB2025XDXM 13) and the National Natural Science Foundation of China (No. 62332016).
ETHICS STATEMENT
This work focuses video generation for autonomous driving research. Our model is trained and evaluated on publicly available datasets (e.g., nuScenes) and is intended solely for academic research. We emphasize that the generated data should not be directly deployed in real-world driving systems without careful validation, as safety-critical applications require rigorous testing.
REPRODUCIBILITY STATEMENT
We provide comprehensive details the model architecture, training objectives, and evaluation protocols. All datasets used in this work are publicly available, and the implementation details carefully documented to ensure reproducibility. Code and configuration files are included in the supplementary material to facilitate further research and independent verification.
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Reviews:
[{"review_id": "oPtuzHMJ6k", "reviewer": "Reviewer_4E4z", "summary": "This paper introduces ConsisDrive, a driving world model designed to address identity drift—a common failure mode in generative video models where objects change appearance or category across frames.  It achieves state-of-the-art results in FID (3.88) and FVD (37.23), and shows significant improvements in downstream tasks like perception (NDS) and multi-object tracking (IDS), demonstrating strong instance-level temporal consistency.", "questions": "- How does the model handle severe occlusions or interactions between instances that may challenge the instance masking mechanism?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "- Clearly identifies and addresses “identity drift,” a critical yet understudied issue in driving video generation.\n- Instance-Masked Attention effectively enforces instance-level consistency by leveraging identity and trajectory masks.\n- Instance-Masked Loss adaptively balances foreground and background supervision, improving fidelity for small objects.", "weaknesses": "The paper lacks comparisons with several recent SOTA methods, particularly **InstaDrive** [1], which also focuses on the quality of instance-level generation. Including such comparisons would better contextualize the proposed method’s performance and highlight its relative strengths or limitations in generating high-fidelity instances.\n\n[1] Yang Z, Guo X, Ding C, et al. InstaDrive: Instance-Aware Driving World Models for Realistic and Consistent Video Generation[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. 2025: 25410-25420.", "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": "m8BQqWdrmQ", "reviewer": "Reviewer_6KzT", "summary": "This work presents a good solution for improving instance-level temporal consistency in autonomous driving scene video generation. It identifies an important problem of high-cost real data collection in autonomous driving and demonstrates good performances through experiments. However, its lack of original methodological contributions makes it insufficient acceptance. The core ideas are adaptations or combinations of existing techniques to a specific scene, rather than novel frameworks.", "questions": "Q1. Open Sora 2.0 and ControlNet seem not to be the best option, have the authors tried other SOTA foundation models like Wan (2.1/2.2)? The reviewer thinks that Wan 2.1 has relatively good performances regarding identity preserving.\n\nQ2. Please report the computational cost of the framework and comparative methods, e.g., training epochs, total training time, inference latency, FLOPS (I know that the authors mention it in Appendix E, but I wonder what exactly it is).\n\nQ3. In Fig. 2(c), The text (Frame t and Frame t+1) is oriented vertically to the right, but the image is oriented vertically to the left.\n\nQ4. In the second and third parts of quantitative analysis subsection, the authors mainly use the generated videos as the sole or additional training data except for Table 2 (correct me if I’m mistaken), I wonder what the results will be if the authors directly generate videos from the validation set and conduct experiments on the MOT task.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 2, "strengths": "S1. This work identifies instance identity drift (including category shifts, color inconsistencies, foreground dilution) as a serious issue for driving-oriented synthetic data, and provides solutions to the unique demands of driving scenes.\n\nS2. The experimental results do show advantages over current approaches. Evaluations on downstream tasks are included, providing an important insight that incorporating synthetic data helps to improve performances on downstream tasks. Also, ablations verify the importance of each module.\n\nS3. The paper is easy to follow.\n\nS4. The authors provide sufficient materials for reproducibility, which fosters the research community.", "weaknesses": "W1. Limited Novelty. I would say that this is a great engineering work with a reasonable pipeline and should produce good results, but lacks significant distinction between this work and previous works. For example, CineMaster proposes using 3D depth box and class labels to achieve semantic layout control with ControlNet, which is quite similar to this work, in my opinion. Also, the authors claim that they propose instance-masked attention and instance-masked loss. However, neither are ground-breaking in computer vision. 1) Instance-masked attention only uses masked attention, which is a common practice in computer vision (e.g., Mask2Former), and the authors only ADAPT it to the autonomous driving scene. 2) Instance-masked loss is designed to force the models to focus on certain areas, whose idea is similar to Focal Loss, which have been used in many classic computer vision tasks.\n\nW2. Not So Comprehensive Experimental Setup. Although the authors present experimental results on multiple downstream tasks, the authors mainly train and test on the nuScenes dataset (on the video generation task, the main claim), which raises doubts about the generalization ability of the framework, especially lacking of cross-dataset validation, even in the supplementary materials. I would suggest that the authors benchmark on other autonomous video generation datasets (e.g., on private dataset in the supplementary materials and report the results).", "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": "BTuVAxvC6I", "reviewer": "Reviewer_JFL4", "summary": "This paper proposes ConsisDrive, an identity-preserving world model for driving scene video generation, which aims to address the \"identity drift\" problem. The core contribution lies in the introduction of two instance-aware mechanisms: Instance-Masked Attention (IMA) and Instance-Masked Loss (IML). IMA utilizes identity masks and trajectory masks to enforce visual tokens to exclusively interact with features of their corresponding instances across both spatial and temporal dimensions, thereby ensuring the consistency of instance attributes. IML employs a probabilistic dynamic masking strategy to adaptively emphasize supervision on foreground regions, reducing interference from background noise. Experimental results demonstrate that ConsisDrive achieves state-of-the-art video generation quality (lower FID/FVD) on the nuScenes dataset and shows significant performance improvements on downstream perception tasks.", "questions": "Please refer the above Weaknesses.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The proposed IMA presents a simple yet effective solution by integrating instance-level identity conditioning and cross-frame propagation into the Transformer's 3D self-attention mechanism via instance identity masks and instance trajectory masks.\n2. The evaluation is comprehensive. Beyond standard video generation metrics (FID, FVD), the paper thoroughly assesses the utility of the generated data through downstream tasks, including perception and multi-object tracking.\n3. The video results in the supplementary materials are impressive and highly realistic.", "weaknesses": "1. The necessity and advantages of injecting instance attributes (category, size, tracking ID) as a global condition Ginto the attention mechanism via the Instance Identity Mask, compared to traditional conditioning approaches in diffusion models, require deeper discussion and justification.\n2. It needs a clearer rationale for why encoding these instance attributes into a global embedding Gand interacting via the Identity Mask Mk,m+iis superior to alternative conditioning strategies, such as injecting them directly as tokens into the value V or using an additional cross-attention layer.\n3. The probabilistic dynamic masking strategy (with probability α) in IML is crucial for balancing foreground and global reconstruction. However, there is a lack of ablation studies on the choice or impact of α.", "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": "zi4OKnepGx", "reviewer": "Reviewer_V1rg", "summary": "This paper presents ConsisDrive, a novel video diffusion model that incorporates instance-specific attention to improve instance consistency in autonomous driving scenarios. To address identity drift, the method introduces an instance global identity condition embedding that represents all instances in the scene. For each instance, specialized attention is applied to patches corresponding to the instance mask, and the output is fused with the standard attention output through gated addition.", "questions": "Please refer to the weaknesses.", "limitations": "", "rating": 2, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The paper focuses on addressing the important problem of instance consistency in driving scene generation.\n2. Experimental results demonstrate that ConsisDrive achieves strong performance on key metrics.", "weaknesses": "1. This method adopts full attention, which is very costing\n2. Insufficient literature review: The paper does not includerecent driving world models published in late 2024 and early 2025. I list some of them: [UniMLVG](https://arxiv.org/abs/2412.04842), [UniScene](https://arxiv.org/abs/2412.05435), [DrivingSphere](https://arxiv.org/abs/2411.11252), and [DiVE](https://arxiv.org/abs/2409.01595). Notably, UniMLVG and UniScene are open-source, and UniMLVG reports better FVD performance.\n3. The current results do not conclusively demonstrate that instance mask attention is the primary contributor to improved instance consistency. The improvement is mainly shown in the quantitative metrics. While Figure 3 suggests the complete pipeline improves instance-level consistency, the compared baselines differ from ConsisDrive in multiple dimensions, including model architecture, pretrained models, and conditioning inputs. The authors should isolate the contribution of the proposed attention in the qualitative results. Additionally, I recommend that the authors provide some challenging cases, such as vehicles that temporarily disappear and later reappear in the scene.", "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)