Pith. sign in

REVIEW 4 major objections 5 minor 5 cited by

WorldLens: Full-Spectrum Evaluations of Driving World Models in Real World

T0 review · 4 major / 5 minor · reviewed 2026-08-03 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read WorldLens claims no current driving world model excels universally: perceptual realism and functional usability are decoupled, so a full-spectrum benchmark must measure both appearance and behavior.

desk verdict A serious, mostly well-built benchmark for driving world models, with one real confound in the downstream and action metrics that should be controlled before the headline claim is taken at face value. read the letter →

arxiv 2512.10958 v2 pith:5PSTYZAH submitted 2025-12-11 cs.CV

Ao Liang , Lingdong Kong , Tianyi Yan , Hongsi Liu , Wesley Yang , Ziqi Huang , Wei Yin , Jialong Zuo
show 14 more authors
Yixuan Hu Dekai Zhu Dongyue Lu Youquan Liu Guangfeng Jiang Linfeng Li Xiangtai Li Long Zhuo Lai Xing Ng Benoit R. Cottereau Changxin Gao Liang Pan Wei Tsang Ooi Ziwei Liu
This is my paper · ORCID
classification cs.CV
keywords worldmodelevaluationdrivingmodelsbenchmarkgenerativevideohumanpreferenceclosed-loopsimulation4Dreconstructionagent
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

WorldLens is a benchmark for driving world models that claims to be the first to measure both appearance and behavior. It scores six models across five aspects—generation, reconstruction, action-following, downstream tasks, and human preference—spanning 24 dimensions. Its central finding is that no model excels everywhere: the best-looking model ranks second-lowest in 3D detection and lowest in tracking, while all models finish only 6.89–13.51% of closed-loop routes, and human raters give overall realism an average of 2–3 out of 10. The paper argues that this decoupling makes unified, human-aligned evaluation essential, and it provides one: a 26,808-annotation dataset and a distilled, explainable evaluation agent.

What carries the argument

The carrying mechanism is a five-aspect, 24-dimension protocol that pairs objective signals—monocular depth stability, semantic label stability, 4D Gaussian-splatting reconstructability, frozen-planner trajectory adherence, closed-loop route completion, and frozen-perception downstream scores—with a large human-annotated preference dataset of 26,808 scored videos with textual rationales. A vision-language critic distilled from these annotations outputs both 1–10 scores and evidence-based explanations, enabling scalable, explainable evaluation. The key move is testing each model both as an appearance generator and as an environment that a planner can operate in, which exposes the appearance–b

What would settle it

Take real driving frames, apply distribution shifts that mimic each generated model's visual style, and run the same frozen perception stack; if detection and tracking scores fall as much as they do on generated videos, the downstream aspect mainly measures perception-model brittleness rather than world-model fidelity.

Watch

Extended reading notes

Core claim

The paper's central claim is that perceptual quality and functional usability are decoupled in current driving world models. Empirically, the model with the highest subject fidelity scores poorly on downstream detection and tracking, while the geometrically most stable model is also the most balanced overall yet still fails to complete more than 13.51% of closed-loop routes. Human ratings of world realism, physical plausibility, and behavioral safety cluster around 2–3 out of 10 for every model, with a strong correlation between perceived realism and geometric consistency. WorldLens makes these trade-offs visible and reproducible through a standardized five-aspect protocol.

Load-bearing premise

Each aspect score is interpreted as a property of the world model, but no control isolates the external toolchain—the 4D reconstruction optimizer, traffic simulator, and frozen perception models—that co-determines every score.

Editorial extensions

If this is right

  • If perceptual and functional decoupling holds, optimizing only appearance metrics will not produce safe driving simulators; geometry and temporal conditioning must be explicit objectives.
  • A common five-aspect protocol makes results across models and datasets comparable, standardizing world-model evaluation.
  • The distilled evaluation agent can replace costly human annotation, returning both scores and reasons at scale.
  • The uniformly low human ratings (2–3/10) quantify a large headroom for improvement, not just incremental gains.
  • Geometry-aware supervision consistently improves reconstruction, novel-view, and downstream scores, pointing to a concrete design direction.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The toolchain confound implies that downstream scores may partly reflect how brittle the frozen perception models are to each model's synthetic distribution; a calibration run on real frames perturbed to match each style would separate the two effects.
  • The strong correlation between human realism ratings and geometric consistency suggests depth and novel-view metrics could serve as a cheap proxy for human preference, lowering annotation cost.
  • WorldLens-26K could be repurposed as a reward model for reinforcement fine-tuning of world models, turning the benchmark from a measurement stick into an optimization target.
  • The closed-loop collapse below 14% route completion indicates a ceiling not visible in open-loop metrics; testing whether self-forcing or streaming-diffusion training raises that ceiling is a direct next step.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. WorldLens proposes a five-aspect benchmark (Generation, Reconstruction, Action-Following, Downstream Task, Human Preference) with 24 metrics for evaluating driving world models. On six recent models (MagicDrive, DreamForge, DriveDreamer-2, OpenDWM, DiST-4D, X-Scene), the paper reports that no model dominates across all aspects: OpenDWM leads subject fidelity while DiST-4D tends to lead geometry, reconstruction, and downstream perception; open-loop action-following is moderate, closed-loop route completion is uniformly low (6.89–13.51%), and human ratings cluster around 2–3/10. To align automated evaluation with human judgment, the authors collect WorldLens-26K, a dataset of 26,808 human-annotated score/rationale records, and train WorldLens-Agent, a Qwen3-VL-based critic that predicts scores and generates textual rationales. The central claims are that visual realism does not imply behavioral usability and that geometry-aware, temporally conditioned generation yields more physically coherent worlds.

Significance. The benchmark is timely and the empirical breadth is substantial. Strengths include the detailed per-dimension appendices, the transparent use of fixed pretrained evaluators, the unusually large human annotation dataset with structured rationales, and the stated commitment to release toolkit, dataset, and model. If the measurement-validity issues are addressed, WorldLens could become a useful standardized evaluation ecosystem for driving world models. At present, however, the headline conclusion 'Perceptual Quality Does Not Imply Usability' is not yet established, because the downstream metrics conflate world-model fidelity with the domain robustness of the frozen perception models. Likewise, the claim that WorldLens-Agent shows 'strong alignment with human annotations' is supported only by qualitative examples. Both issues are fixable with additional analyses, but they are load-bearing for the paper's main contributions.

major comments (4)
  1. [§3.4, §5.3, Tables 3 and 21] The downstream-task aspect uses a single frozen perception toolchain (BEVFusion for D.1/D.2, ADA-Track for D.3, SparseOcc for D.4) pretrained on real nuScenes frames. The paper interprets OpenDWM's low NDS (21.96% vs. DiST-4D's 33.22%) and the statement 'large-scale multi-domain training can hinder adaptation' as evidence about the world model. An equally plausible reading is that these detectors are brittle to OpenDWM's particular synthetic distribution (color, contrast, resolution, object appearance), independent of scene fidelity. No control is provided that perturbs real frames to match each world model's distribution, or that uses multiple perception backbones, or that measures a domain-gap baseline. This confound bears directly on the headline 'Perceptual Quality Does Not Imply Usability' in §5.3. The limitation section (§13.3) does not flag this issue. I recommend adding a control
  2. [§4.3, §5.2, Figure 8, §12.4] The paper claims that WorldLens-Agent's predicted scores 'exhibit strong alignment with human annotations across all evaluated dimensions,' but no quantitative evidence is reported. Section 5.2 and Appendix 12.4 show only qualitative examples on Gen3C videos. There is no correlation coefficient (e.g., Spearman or Pearson), no per-dimension agreement statistics, no sample size for the zero-shot test, and no comparison with the base Qwen3-VL model without LoRA fine-tuning. Since WorldLens-Agent and WorldLens-26K are billed as a core contribution that enables 'scalable, explainable scoring,' the absence of a quantitative validation is a load-bearing gap. Please report agreement metrics on a held-out or OOD set, ideally with confidence intervals and a base-model baseline.
  3. [§3.3, Table 2, §9.2.3] The Action-Following scores are co-determined by the LimSim traffic engine and the DriveArena closed-loop protocol, so the uniformly low Route Completion rates (6.89–13.51%) may partly reflect simulator and planner limitations rather than world-model deficiencies. More importantly, the two models featured in the paper's main trade-off narrative — OpenDWM and DiST-4D — are absent from Table 2, so the claim that 'geometry-stable ones lack behavioral fidelity' is not directly tested for the models that are central to the headline. Either include these models in the closed-loop evaluation or restrict the claim to the models actually evaluated. I also suggest a real-data closed-loop baseline (e.g., same planner and simulator on real recorded frames) to calibrate the absolute route-completion numbers.
  4. [§4.1, §5.2, Tables 24–29] The human preference scores are heavily concentrated at the low end: for most dimensions the median and quartiles are all 2.0, with mean differences between models often around 0.2–0.3 points. The paper nevertheless makes comparative claims such as DiST-4D 'achieves the most balanced scores' and 'leads in physical plausibility' and 'behavioral safety.' No inter-annotator agreement, significance testing, or confidence intervals are reported, and the number of unique videos per model is not stated. Given the small apparent effect sizes, these comparative human-preference claims are not statistically supported. Please add agreement metrics and a statistical analysis of the model-level differences.
minor comments (5)
  1. [Table 21] The OpenDWM mAP value reads '0.944'; from the NDS value and surrounding rows this appears to be a typo for '0.0944'. Please correct.
  2. [Tables 1–3] The 'Empirical Max' row is not defined in the main text. State whether it is a per-sample maximum, a video-level upper bound, or an oracle value, and clarify why some cells are omitted (e.g., Perceptual Discrepancy).
  3. [§2] The phrase 'the first benchmark that measures both the appearance and behavior' is too strong given existing closed-loop and behavior-oriented evaluations such as DriveArena and NAVSIM. Please qualify the novelty claim.
  4. [§5.2] The statement that 'World Realism and Consistency scores correlate strongly' is made without reporting a correlation coefficient or scatter plot. Please add the quantitative value.
  5. [§11.5] The section title says 'Physical Plausibility' but the content and rubric describe 3D & 4D Consistency; the heading appears to be a copy-paste error.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: WorldLens is a measurement benchmark whose rankings come from frozen external evaluators and ground-truth comparisons, not from fitted parameters renamed as predictions.

full rationale

WorldLens is not a derivation chain but an evaluation protocol. Each aspect is a defined measurement on fixed generated videos: Generation scores use frozen classifiers/encoders (ViT, DINO, CLIP, I3D, Depth Anything, LoFTR); Reconstruction compares re-renderings against input or ground-truth reconstructions; Action-Following uses pretrained planners and the NAVSIM/DriveArena protocols; Downstream Task applies frozen BEVFusion/ADA-Track/SparseOcc to generated frames; Human Preference is direct annotation. No metric is fitted to the data and then reported as a prediction, and no equation reduces one reported quantity to another by construction. The WorldLens-Agent is explicitly a distilled supervised evaluator trained on WorldLens-26K and validated zero-shot on Gen3C; it is not used to derive the paper's cross-model conclusions. Self-citations (e.g., the 3D/4D world modeling survey [53], VBench [41,42], Evaluation Agent [137]) are contextual and do not carry the load-bearing argument. The main substantive concern—that Downstream Task scores may partly reflect the brittleness of frozen perception models to particular synthetic distributions—is a measurement-validity confound, not a circularity: the paper never equates detector score to world-model fidelity by definition, and the reported rankings are genuine outputs of external tools applied to generated videos. Since every load-bearing claim is backed by externally grounded measurement or human annotation, the appropriate finding is no significant circularity.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

No new physical entities or mediators are postulated. The free parameters are disclosed tuning choices that do not load-bear the central claims. The load-bearing assumptions are all about measurement validity: the frozen-models-as-proxies assumption, the downstream-brittleness confound, unquantified annotator reliability, the fairness of the OmniRe reconstruction step, and the absence of error bars.

free parameters (5)
  • Semantic Consistency weights (w1, w2, w3) = (0.5, 0.4, 0.1)
    Hand-chosen weighting of Label Flip Rate, Segment Association, and Class Distribution Stability, Eq. (6) in §7.6.2; affects G.6 ranking magnitudes but not the main qualitative conclusions.
  • MRS exponent beta and epsilon = beta = 0.5, epsilon = 1e-8
    Hand-set constants in the motion-rate similarity and temporal jitter formulas (§7.5.2) used by G.3 and G.5; disclosed but arbitrary.
  • ReID confidence thresholds for frame filtering = 0.25 vehicle / 0.50 pedestrian
    Determines which frames enter the G.2 Subject Coherence aggregation (§7.2.3); chosen by hand.
  • Novel-view lateral offset = 1 m
    Defines the lateral_offset trajectory in R.3/R.4 (§8.3.1); an arbitrary choice affecting reconstruction scores.
  • Agent training hyperparameters = LoRA rank 16, dropout 0.05, 3 epochs, lr 1e-4, 8x A100
    Disclosed SFT configuration (§12.3); no ablation shows sensitivity of the evaluator's quality to these choices.
assumptions (5)
  • domain assumption Pretrained models' outputs are faithful proxies for the world-model qualities they stand in for
    Every metric in Aspects 1-2 reduces to features from external frozen models (e.g., G.4 depth via Video DepthAnything §7.4.3; G.8 LoFTR confidences §7.8; G.7 FVD on I3D). If these models fail on synthetic distributions, scores reflect toolchain behavior rather than world-model quality.
  • domain assumption Downstream task scores measure world-model utility rather than the perception models' domain-shift brittleness
    Aspects 3-4 apply UniAD/VAD, BEVFusion, ADA-Track, and SparseOcc, all trained on real data, to synthetic video (§3.3, §3.4, §9, §10). OpenDWM's low detection score (21.9%) is interpreted as a world-model property (§5.1), but it could reflect perception-model fragility to OpenDWM's distribution; no control experiment rules this out.
  • domain assumption Annotator reliability: two groups of five produce reference-quality ratings
    §4.1 describes re-evaluation on divergence but reports no kappa or ICC; the WorldLens-26K labels and the agent's supervision rest on this unquantified reliability assumption.
  • domain assumption OmniRe 4DGS optimization is fair and converges comparably across all models' videos
    Aspect 2 (§8.1.3) trains one 30k-step 4D Gaussian field per clip; videos from different models have different artifacts, so 'reconstructability' is a joint property of world model plus optimizer.
  • domain assumption Point estimates without error bars support the cross-model comparisons
    All benchmark tables report single-run numbers with no uncertainty or significance tests (e.g., FVD values in Table 10); rankings are read as deterministic.

how reviews work

0 comments
Cite this review

Pith. "Pith review of WorldLens: Full-Spectrum Evaluations of Driving World Models in Real World." pith.science (2026). https://pith.science/paper/5PSTYZAH

@misc{pith2026251210958,
  author       = {Pith},
  title        = {Pith review of: WorldLens: Full-Spectrum Evaluations of Driving World Models in Real World},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5PSTYZAH}},
  note         = {Machine review of arXiv:2512.10958}
}
read the original abstract

Generative world models are reshaping embodied AI, enabling agents to synthesize realistic 4D driving environments that look convincing but often fail physically or behaviorally. Despite rapid progress, the field still lacks a unified way to assess whether generated worlds preserve geometry, obey physics, or support reliable control. We introduce WorldLens, a full-spectrum benchmark evaluating how well a model builds, understands, and behaves within its generated world. It spans five aspects -- Generation, Reconstruction, Action-Following, Downstream Task, and Human Preference -- jointly covering visual realism, geometric consistency, physical plausibility, and functional reliability. Across these dimensions, no existing world model excels universally: those with strong textures often violate physics, while geometry-stable ones lack behavioral fidelity. To align objective metrics with human judgment, we further construct WorldLens-26K, a large-scale dataset of human-annotated videos with numerical scores and textual rationales, and develop WorldLens-Agent, an evaluation model distilled from these annotations to enable scalable, explainable scoring. Together, the benchmark, dataset, and agent form a unified ecosystem for measuring world fidelity -- standardizing how future models are judged not only by how real they look, but by how real they behave.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. WorldRoamBench: An Open-World Benchmark for Long-Horizon Stability of Interactive World Models

    cs.CV 2026-06 unverdicted novelty 7.0 of 10

    WorldRoamBench is a new benchmark for interactive world models that evaluates four stability dimensions with custom metrics and finds no tested model performs reliably across all.

  2. WBench: A Comprehensive Multi-turn Benchmark for Interactive Video World Model Evaluation

    cs.CV 2026-05 unverdicted novelty 7.0 of 10

    WBench is a benchmark with 289 test cases and 1,058 turns for evaluating interactive world models using 22 automated metrics validated against human judgments.

  3. World Models as Group Actions

    cs.CV 2026-05 unverdicted novelty 7.0 of 10

    Formalizes video world models as group actions on states and uses latent regularization with synthesized supervision to enforce consistency, introducing GAC and GAR metrics that improve structural correctness in SOTA models.

  4. StressDream: Steering Video World Models for Robust Policy Evaluation and Improvement

    cs.CV 2026-05 unverdicted novelty 6.0 of 10

    StressDream optimizes initial noise in diffusion video world models using VLM semantic and plausibility objectives to steer generations toward specified high-impact outcomes for improved policy evaluation.

  5. IRIS: A Real-World Benchmark for Inverse Recovery and Identification of Physical Dynamic Systems from Monocular Video

    cs.CV 2026-03 accept novelty 6.0 of 10

    IRIS releases 220 real 4K videos of eight dynamical systems with ground-truth parameters plus a protocol that measures parameter recovery, equation selection, and multi-body failure modes of unsupervised video-to-phys...

Reference graph

Works this paper leans on

149 extracted references · 39 linked inside Pith · cited by 5 Pith papers

  1. [1]

    ACT-Bench: Towards action controllable world models for autonomous driving.arXiv preprint arXiv:2412.05337, 2024

    Hidehisa Arai, Keishi Ishihara, Tsubasa Takahashi, and Yu Yamaguchi. ACT-Bench: Towards action controllable world models for autonomous driving.arXiv preprint arXiv:2412.05337, 2024

    arXiv 2024

  2. [2]

    Qwen2.5-vl technical report.arXiv preprint arXiv:2502.13923, 2025

    Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, Humen Zhong, Yuanzhi Zhu, Mingkun Yang, Zhaohai Li, Jianqiang Wan, Pengfei Wang, Wei Ding, Zheren Fu, Yiheng Xu, Jiabo Ye, Xi Zhang, Tianbao Xie, Zesen Cheng, Hang Zhang, Zhibo Yang, Haiyang Xu, and Junyang Lin. Qwen2.5-vl technical report.ar...

    arXiv 2025

  3. [3]

    Philip J. Ball, Jakob Bauer, Frank Belletti, Bethanie Brownfield, Ariel Ephrat, Shlomi Fruchter, Agrim Gupta, Kristian Holsheimer, Aleksander Holynski, Jiri Hron, Christos Kaplanis, Marjorie Limont, Matt McGill, Yanko Oliveira, Jack Parker-Holder, Frank Perbet, Guy Scully, Jeremy Shar, Stephen Spencer, Omer Tov, Ruben Villegas, Emma Wang, Jessica Yung, Ci...

    2025

  4. [4]

    Lumiere: A space-time diffusion model for video generation

    Omer Bar-Tal, Hila Chefer, Omer Tov, Charles Herrmann, Roni Paiss, Shiran Zada, Ariel Ephrat, Junhwa Hur, Guanghui Liu, Amit Raj, Yuanzhen Li, Michael Rubinstein, Tomer Michaeli, Oliver Wang, Deqing Sun, Tali Dekel, and Inbar Mosseri. Lumiere: A space-time diffusion model for video generation. InSIGGRAPH Asia, pages 1–11, 2024

    2024

  5. [5]

    VaViM and VaVAM: Autonomous driving through video generative modeling.arXiv preprint arXiv:2502.15672, 2025

    Florent Bartoccioni, Elias Ramzi, Victor Besnier, Shashanka Venkataramanan, Tuan-Hung Vu, Yihong Xu, Loick Chambon, Spyros Gidaris, Serkan Odabas, David Hurych, Renaud Marlet, Alexandre Boulch, Mickael Chen, Éloi Zablocki, Andrei Bursuc, Eduardo Valle, and Matthieu Cord. VaViM and VaVAM: Autonomous driving through video generative modeling.arXiv preprint ...

    arXiv 2025

  6. [6]

    Improving image generation with better captions.Computer Science, 2(3):8, 2023

    James Betker, Gabriel Goh, Li Jing, Tim Brooks, Jianfeng Wang, Linjie Li, Long Ouyang, Juntang Zhuang, Joyce Lee, Yufei Guo, et al. Improving image generation with better captions.Computer Science, 2(3):8, 2023

    2023

  7. [7]

    DynamicCity: Large-scale 4D occupancy generation from dynamic scenes

    Hengwei Bian, Lingdong Kong, Haozhe Xie, Liang Pan, Yu Qiao, and Ziwei Liu. DynamicCity: Large-scale 4D occupancy generation from dynamic scenes. InInternational Conference on Learning Representations, 2025

    2025

  8. [8]

    Align your latents: High-resolution video synthesis with latent diffusion models

    Andreas Blattmann, Robin Rombach, Huan Ling, Tim Dockhorn, Seung Wook Kim, Sanja Fidler, and Karsten Kreis. Align your latents: High-resolution video synthesis with latent diffusion models. InIEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22563–22575, 2023

    2023

Show all 149 references
  1. [9]

    nuScenes: A multimodal dataset for autonomous driving

    Holger Caesar, Varun Bankiti, Alex H Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom. nuScenes: A multimodal dataset for autonomous driving. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11...

    2020

  2. [10]

    nuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles

    Holger Caesar, Juraj Kabzan, Kok Seang Tan, Whye Kit Fong, Eric Wolff, Alex Lang, Luke Fletcher, Oscar Beijbom, and Sammy Omari. nuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles. arXiv preprint arXiv:2106.11810, 2021. 30https://github.com/PJLab-ADG/Ope...

    2021 arXiv

  3. [11]

    Pseudo-simulation for autonomous driving

    Wei Cao, Marcel Hallgarten, Tianyu Li, Daniel Dauner, Xunjiang Gu, Caojun Wang, Yakov Miron, Marco Aiello, Hongyang Li, Igor Gilitschenski, Boris Ivanovic, Marco Pavone, Andreas Geiger, and Kashyap Chitta. Pseudo-simulation for autonomous driving. InConference on Robot Learnin...

    2025

  4. [12]

    Emerging properties in self-supervised vision transformers

    Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. InIEEE/CVF International Conference on Computer Vision, pages 9650–9660, 2021

    2021

  5. [13]

    Quo vadis, action recognition? A new model and the kinetics dataset

    Joao Carreira and Andrew Zisserman. Quo vadis, action recognition? A new model and the kinetics dataset. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6299–6308, 2017

    2017

  6. [14]

    GeoDrive: 3D geometry-informed driving world model with precise action control.arXiv preprint arXiv:2505.22421, 2025

    Anthony Chen, Wenzhao Zheng, Yida Wang, Xueyang Zhang, Kun Zhan, Peng Jia, Kurt Keutzer, and Shanghang Zhang. GeoDrive: 3D geometry-informed driving world model with precise action control.arXiv preprint arXiv:2505.22421, 2025

    2025 arXiv

  7. [15]

    Video depth anything: Consistent depth estimation for super-long videos

    Sili Chen, Hengkai Guo, Shengnan Zhu, Feihu Zhang, Zilong Huang, Jiashi Feng, and Bingyi Kang. Video depth anything: Consistent depth estimation for super-long videos. InIEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22831–22840, 2025

    2025

  8. [16]

    DrivingGPT: Unifying driving world modeling and planning with multi-modal autoregressive transformers.arXiv preprint arXiv:2412.18607, 2024

    Yuntao Chen, Yuqi Wang, and Zhaoxiang Zhang. DrivingGPT: Unifying driving world modeling and planning with multi-modal autoregressive transformers.arXiv preprint arXiv:2412.18607, 2024

    2024 arXiv

  9. [17]

    OmniRe: Omni urban scene reconstruction

    Ziyu Chen, Jiawei Yang, Jiahui Huang, Riccardo de Lutio, Janick Martinez Esturo, Boris Ivanovic, Or Litany, Zan Gojcic, Sanja Fidler, Marco Pavone, Li Song, and Yue Wang. OmniRe: Omni urban scene reconstruction. InInternational Conference on Learning Representations, 2025

    2025

  10. [18]

    Self-forcing++: Towards minute-scale high-quality video generation.arXiv preprint arXiv:2510.02283, 2025

    Justin Cui, Jie Wu, Ming Li, Tao Yang, Xiaojie Li, Rui Wang, Andrew Bai, Yuanhao Ban, and Cho-Jui Hsieh. Self-forcing++: Towards minute-scale high-quality video generation.arXiv preprint arXiv:2510.02283, 2025

    2025 arXiv

  11. [19]

    Histograms of oriented gradients for human detection

    Navneet Dalal and Bill Triggs. Histograms of oriented gradients for human detection. InIEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 886–893, 2005

    2005

  12. [20]

    NAVSIM: Data-driven non-reactive autonomous vehicle simulation and benchmarking

    Daniel Dauner, Marcel Hallgarten, Tianyu Li, Xinshuo Weng, Zhiyu Huang, Zetong Yang, Hongyang Li, Igor Gilitschenski, Boris Ivanovic, Marco Pavone, Andreas Geiger, and Kashyap Chitta. NAVSIM: Data-driven non-reactive autonomous vehicle simulation and benchmarking. InAdvances i...

    2024

  13. [21]

    ImageNet: A large-scale hierarchical image database

    Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. ImageNet: A large-scale hierarchical image database. InIEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 248–255, 2009

    2009

  14. [22]

    ADA-Track: End-to-end multi-camera 3D multi-object tracking with alternating detection and association

    Shuxiao Ding, Lukas Schneider, Marius Cordts, and Juergen Gall. ADA-Track: End-to-end multi-camera 3D multi-object tracking with alternating detection and association. InIEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15184–15194, 2024

    2024

  15. [23]

    CARLA: An open urban driving simulator

    Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun. CARLA: An open urban driving simulator. InConference on Robot Learning, pages 1–16. PMLR, 2017

    2017

  16. [24]

    An image is worth 16x16 words: Transformers for image recognition at scale

    Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at...

    2021

  17. [25]

    WorldScore: A unified evaluation benchmark for world generation.arXiv preprint arXiv:2504.00983, 2025

    Haoyi Duan, Hong-Xing Yu, Sirui Chen, Li Fei-Fei, and Jiajun Wu. WorldScore: A unified evaluation benchmark for world generation.arXiv preprint arXiv:2504.00983, 2025

    2025

  18. [26]

    Vchitect-2.0: Parallel transformer for scaling up video diffusion models.arXiv preprint arXiv:2501.08453, 2025

    Weichen Fan, Chenyang Si, Junhao Song, Zhenyu Yang, Yinan He, Long Zhuo, Ziqi Huang, Ziyue Dong, Jingwen He, Dongwei Pan, et al. Vchitect-2.0: Parallel transformer for scaling up video diffusion models.arXiv preprint arXiv:2501.08453, 2025

    2025 arXiv

  19. [27]

    MagicDrive: Street view generation with diverse 3D geometry control

    Ruiyuan Gao, Kai Chen, Enze Xie, Lanqing Hong, Zhenguo Li, Dit-Yan Yeung, and Qiang Xu. MagicDrive: Street view generation with diverse 3D geometry control. InInternational Conference on Learning Representations, 2023

    2023

  20. [28]

    MagicDrive-V2: High-resolution long video generation for autonomous driving with adaptive control

    Ruiyuan Gao, Kai Chen, Bo Xiao, Lanqing Hong, Zhenguo Li, and Qiang Xu. MagicDrive-V2: High-resolution long video generation for autonomous driving with adaptive control. InIEEE/CVF International Conference on Computer Vision, pages 28135–28144, 2025. 73

    2025

  21. [29]

    Vista: A generalizable driving world model with high fidelity and versatile controllability

    Shenyuan Gao, Jiazhi Yang, Li Chen, Kashyap Chitta, Yihang Qiu, Andreas Geiger, Jun Zhang, and Hongyang Li. Vista: A generalizable driving world model with high fidelity and versatile controllability. InAdvances in Neural Information Processing Systems, volume 37, 2024

    2024

  22. [30]

    DiST-4D: Disentangled spatiotemporal diffusion with metric depth for 4D driving scene generation

    Jiazhe Guo, Yikang Ding, Xiwu Chen, Shuo Chen, Bohan Li, Yingshuang Zou, Xiaoyang Lyu, Feiyang Tan, Xiaojuan Qi, Zhiheng Li, and Hao Zhao. DiST-4D: Disentangled spatiotemporal diffusion with metric depth for 4D driving scene generation. InIEEE/CVF International Conference on C...

    2025

  23. [31]

    MineWorld: A real-time and open-source interactive world model on MineCraft.arXiv preprint arXiv:2504.08388, 2025

    Junliang Guo, Yang Ye, Tianyu He, Haoyu Wu, Yushu Jiang, Tim Pearce, and Jiang Bian. MineWorld: A real-time and open-source interactive world model on MineCraft.arXiv preprint arXiv:2504.08388, 2025

    2025 arXiv

  24. [32]

    GEM: A generalizable ego-vision multimodal world model for fine-grained ego-motion, object dynamics, and scene composition control

    Mariam Hassan, Sebastian Stapf, Ahmad Rahimi, Pedro M B Rezende, Yasaman Haghighi, David Brüggemann, Isinsu Katircioglu, Lin Zhang, Xiaoran Chen, Suman Saha, Marco Cannici, Elie Aljalbout, Botao Ye, Xi Wang, Aram Davtyan, Mathieu Salzmann, Davide Scaramuzza, Marc Pollefeys, Pa...

    2025

  25. [33]

    Deep residual learning for image recognition

    Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 770–778, 2016

    2016

  26. [34]

    TransReID: Transformer-based object re-identification

    Shuting He, Hao Luo, Pichao Wang, Fan Wang, Hao Li, and Wei Jiang. TransReID: Transformer-based object re-identification. InIEEE/CVF international conference on computer vision, pages 15013–15022, 2021

    2021

  27. [35]

    GANs trained by a two-time-scale update rule converge to a local Nash equilibrium.Advances in Neural Information Processing Systems, 30:6629–6640, 2017

    Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. GANs trained by a two-time-scale update rule converge to a local Nash equilibrium.Advances in Neural Information Processing Systems, 30:6629–6640, 2017

    2017

  28. [36]

    Imagen video: High definition video generation with diffusion models.arXiv preprint arXiv:2210.02303, 2022

    Jonathan Ho, William Chan, Chitwan Saharia, Jay Whang, Ruiqi Gao, Alexey Gritsenko, Diederik P Kingma, Ben Poole, Mohammad Norouzi, David J Fleet, et al. Imagen video: High definition video generation with diffusion models.arXiv preprint arXiv:2210.02303, 2022

    2022 arXiv

  29. [37]

    GAIA-1: A generative world model for autonomous driving.arXiv preprint arXiv:2309.17080, 2023

    Anthony Hu, Lloyd Russell, Hudson Yeo, Zak Murez, George Fedoseev, Alex Kendall, Jamie Shotton, and Gianluca Corrado. GAIA-1: A generative world model for autonomous driving.arXiv preprint arXiv:2309.17080, 2023

    2023 arXiv

  30. [38]

    Planning-oriented autonomous driving

    Yihan Hu, Jiazhi Yang, Li Chen, Keyu Li, Chonghao Sima, Xizhou Zhu, Siqi Chai, Senyao Du, Tianwei Lin, Wenhai Wang, Lewei Lu, Xiaosong Jia, Qiang Liu, Jifeng Dai, Yu Qiao, and Hongyang Li. Planning-oriented autonomous driving. InIEEE/CVF Conference on Computer Vision and Patte...

    2023

  31. [39]

    SubjectDrive: Scaling generative data in autonomous driving via subject control

    Binyuan Huang, Yuqing Wen, Yucheng Zhao, Yaosi Hu, Yingfei Liu, Fan Jia, Weixin Mao, Tiancai Wang, Chi Zhang, Chang Wen Chen, Zhenzhong Chen, and Xiangyu Zhang. SubjectDrive: Scaling generative data in autonomous driving via subject control. InAAAI Conference on Artificial Int...

    2025

  32. [40]

    Self forcing: Bridging the train-test gap in autoregressive video diffusion.arXiv preprint arXiv:2506.08009, 2025

    Xun Huang, Zhengqi Li, Guande He, Mingyuan Zhou, and Eli Shechtman. Self forcing: Bridging the train-test gap in autoregressive video diffusion.arXiv preprint arXiv:2506.08009, 2025

    2025 arXiv

  33. [41]

    VBench: Comprehensive benchmark suite for video generative models

    Ziqi Huang, Yinan He, Jiashuo Yu, Fan Zhang, Chenyang Si, Yuming Jiang, Yuanhan Zhang, Tianxing Wu, Qingyang Jin, Nattapol Chanpaisit, Yaohui Wang, Xinyuan Chen, Limin Wang, Dahua Lin, Yu Qiao, and Ziwei Liu. VBench: Comprehensive benchmark suite for video generative models. I...

    2024

  34. [42]

    VBench++: Comprehensive and versatile benchmark suite for video generative models.arXiv preprint arXiv:2411.13503, 2024

    Ziqi Huang, Fan Zhang, Xiaojie Xu, Yinan He, Jiashuo Yu, Ziyue Dong, Qianli Ma, Nattapol Chanpaisit, Chenyang Si, Yuming Jiang, et al. VBench++: Comprehensive and versatile benchmark suite for video generative models.arXiv preprint arXiv:2411.13503, 2024

    2024 arXiv

  35. [43]

    VChain: Chain-of-visual-thought for reasoning in video generation.arXiv preprint arXiv:2510.05094, 2025

    Ziqi Huang, Ning Yu, Gordon Chen, Haonan Qiu, Paul Debevec, and Ziwei Liu. VChain: Chain-of-visual-thought for reasoning in video generation.arXiv preprint arXiv:2510.05094, 2025

    2025 arXiv

  36. [44]

    ADriver-I: A general world model for autonomous driving.arXiv preprint arXiv:2311.13549, 2023

    Fan Jia, Weixin Mao, Yingfei Liu, Yucheng Zhao, Yuqing Wen, Chi Zhang, Xiangyu Zhang, and Tiancai Wang. ADriver-I: A general world model for autonomous driving.arXiv preprint arXiv:2311.13549, 2023. 74

    2023 arXiv

  37. [45]

    VAD: Vectorized scene representation for efficient autonomous driving

    Bo Jiang, Shaoyu Chen, Qing Xu, Bencheng Liao, Jiajie Chen, Helong Zhou, Qian Zhang, Wenyu Liu, Chang Huang, and Xinggang Wang. VAD: Vectorized scene representation for efficient autonomous driving. In IEEE/CVF International Conference on Computer Vision, pages 8340–8350, 2023

    2023

  38. [46]

    DiVE: DiT-based video generation with enhanced control

    Junpeng Jiang, Gangyi Hong, Lijun Zhou, Enhui Ma, Hengtong Hu, Xia Zhou, Jie Xiang, Fan Liu, Kaicheng Yu, Haiyang Sun, Kun Zhan, Peng Jia, and Miao Zhang. DiVE: DiT-based video generation with enhanced control. arXiv preprint arXiv:2409.01595, 2024

    2024 arXiv

  39. [47]

    How far is video generation from world model: A physical law perspective.arXiv preprint arXiv:2411.02385, 2024

    Bingyi Kang, Yang Yue, Rui Lu, Zhijie Lin, Yang Zhao, Kaixin Wang, Gao Huang, and Jiashi Feng. How far is video generation from world model: A physical law perspective.arXiv preprint arXiv:2411.02385, 2024

    2024 arXiv

  40. [48]

    The Kinetics human action video dataset.arXiv preprint arXiv:1705.06950, 2017

    Will Kay, Joao Carreira, Karen Simonyan, Brian Zhang, Chloe Hillier, Sudheendra Vijayanarasimhan, Fabio Viola, Tim Green, Trevor Back, Paul Natsev, et al. The Kinetics human action video dataset.arXiv preprint arXiv:1705.06950, 2017

    2017 arXiv

  41. [49]

    MUSIQ: Multi-scale image quality transformer

    Junjie Ke, Qifei Wang, Yilin Wang, Peyman Milanfar, and Feng Yang. MUSIQ: Multi-scale image quality transformer. InIEEE/CVF International Conference on Computer Vision, pages 5148–5157, 2021

    2021

  42. [50]

    3D Gaussian splatting for real-time radiance field rendering.ACM Transactions on Graphics, 42(4):1–14, 2023

    Bernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, and George Drettakis. 3D Gaussian splatting for real-time radiance field rendering.ACM Transactions on Graphics, 42(4):1–14, 2023

    2023

  43. [51]

    StreamDiffusion: A pipeline-level solution for real-time interactive generation

    Akio Kodaira, Chenfeng Xu, Toshiki Hazama, Takanori Yoshimoto, Kohei Ohno, Shogo Mitsuhori, Soichi Sugano, Hanying Cho, Zhijian Liu, Masayoshi Tomizuka, et al. StreamDiffusion: A pipeline-level solution for real-time interactive generation. InIEEE/CVF International Conference ...

    2025

  44. [52]

    Cottereau, Lai Xing Ng, Yuexin Ma, Wenwei Zhang, Liang Pan, Kai Chen, Ziwei Liu, Weichao Qiu, Wei Zhang, Xu Cao, Hao Lu, Ying-Cong Chen, et al

    Lingdong Kong, Shaoyuan Xie, Hanjiang Hu, Yaru Niu, Wei Tsang Ooi, Benoit R. Cottereau, Lai Xing Ng, Yuexin Ma, Wenwei Zhang, Liang Pan, Kai Chen, Ziwei Liu, Weichao Qiu, Wei Zhang, Xu Cao, Hao Lu, Ying-Cong Chen, et al. The RoboDrive challenge: Drive anytime anywhere in any c...

    2024 arXiv

  45. [53]

    Lingdong Kong, Wesley Yang, Jianbiao Mei, Youquan Liu, Ao Liang, Dekai Zhu, Dongyue Lu, Wei Yin, Xiaotao Hu, Mingkai Jia, Junyuan Deng, Kaiwen Zhang, Yang Wu, Tianyi Yan, Shenyuan Gao, Song Wang, Linfeng Li, Liang Pan, Yong Liu, Jianke Zhu, Wei Tsang Ooi, Steven C. H. Hoi, and...

    2025 arXiv

  46. [54]

    Pika.https://www.pika.art, 2024

    Pika Lab. Pika.https://www.pika.art, 2024

    2024

  47. [55]

    A survey on long video generation: Challenges, methods, and prospects.arXiv preprint arXiv:2403.16407, 2024

    Chengxuan Li, Di Huang, Zeyu Lu, Yang Xiao, Qingqi Pei, and Lei Bai. A survey on long video generation: Challenges, methods, and prospects.arXiv preprint arXiv:2403.16407, 2024

    2024 arXiv

  48. [56]

    DrivingDiffusion: Layout-guided multi-view driving scenarios video generation with latent diffusion model

    Xiaofan Li, Yifu Zhang, and Xiaoqing Ye. DrivingDiffusion: Layout-guided multi-view driving scenarios video generation with latent diffusion model. InEuropean Conference on Computer Vision, pages 469–485. Springer, 2024

    2024

  49. [57]

    K-sort arena: Efficient and reliable benchmarking for generative models via k-wise human preferences

    Zhikai Li, Xuewen Liu, Dongrong Joe Fu, Jianquan Li, Qingyi Gu, Kurt Keutzer, and Zhen Dong. K-sort arena: Efficient and reliable benchmarking for generative models via k-wise human preferences. InIEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9131–9141, 2025

    2025

  50. [58]

    Zhiqi Li, Wenhai Wang, Hongyang Li, Enze Xie, Chonghao Sima, Tong Lu, Qiao Yu, and Jifeng Dai. BEVFormer: Learning bird’s-eye-view representation from LiDAR-camera via spatiotemporal transformers.IEEE Transactions on Pattern Analysis and Machine Intelligence, 47(3):2020–2036, 2025

    2020

  51. [59]

    Perspective- invariant 3D object detection

    Ao Liang, Lingdong Kong, Dongyue Lu, Youquan Liu, Jian Fang, Huaici Zhao, and Wei Tsang Ooi. Perspective- invariant 3D object detection. InIEEE/CVF International Conference on Computer Vision, pages 27725–27738, 2025

    2025

  52. [60]

    LiDARCrafter: Dynamic 4D world modeling from LiDAR sequences

    Ao Liang, Youquan Liu, Yu Yang, Dongyue Lu, Linfeng Li, Lingdong Kong, Huaici Zhao, and Wei Tsang Ooi. LiDARCrafter: Dynamic 4D world modeling from LiDAR sequences. InAAAI Conference on Artificial Intelligence, volume 40, 2026

    2026

  53. [61]

    Evaluation of text-to-video generation models: A dynamics perspective

    Mingxiang Liao, Qixiang Ye, Wangmeng Zuo, Fang Wan, Tianyu Wang, Yuzhong Zhao, Jingdong Wang, and Xinyu Zhang. Evaluation of text-to-video generation models: A dynamics perspective. InAdvances in Neural Information Processing Systems, volume 37, pages 109790–109816, 2024

    2024

  54. [62]

    DriveGen: Generalized and robust 3D detection in driving via controllable text-to-image diffusion generation

    Hongbin Lin, Zilu Guo, Yifan Zhang, Shuaicheng Niu, Yafeng Li, Ruimao Zhang, Shuguang Cui, and Zhen Li. DriveGen: Generalized and robust 3D detection in driving via controllable text-to-image diffusion generation. In IEEE/CVF Conference on Computer Vision and Pattern Recogniti...

    2025

  55. [63]

    A survey of AI-generated video evaluation.arXiv preprint arXiv:2410.19884, 2024

    Xiao Liu, Xinhao Xiang, Zizhong Li, Yongheng Wang, Zhuoheng Li, Zhuosheng Liu, Weidi Zhang, Weiqi Ye, and Jiawei Zhang. A survey of AI-generated video evaluation.arXiv preprint arXiv:2410.19884, 2024

    2024

  56. [64]

    EvalCrafter: Benchmarking and evaluating large video generation models

    Yaofang Liu, Xiaodong Cun, Xuebo Liu, Xintao Wang, Yong Zhang, Haoxin Chen, Yang Liu, Tieyong Zeng, Raymond Chan, and Ying Shan. EvalCrafter: Benchmarking and evaluating large video generation models. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 221...

    2024

  57. [65]

    La La LiDAR: Large-scale layout generation from LiDAR data

    Youquan Liu, Lingdong Kong, Weidong Yang, Xin Li, Ao Liang, Runnan Chen, Ben Fei, and Tongliang Liu. La La LiDAR: Large-scale layout generation from LiDAR data. InAAAI Conference on Artificial Intelligence, volume 40, 2026

    2026

  58. [66]

    Rus, and Song Han

    Zhijian Liu, Haotian Tang, Alexander Amini, Xinyu Yang, Huizi Mao, Daniela L. Rus, and Song Han. BEVFusion: Multi-task multi-sensor fusion with unified bird’s-eye view representation. InIEEE International Conference on Robotics and Automation, pages 2774–2781, 2023

    2023

  59. [67]

    WoVoGen: World volume-aware diffusion for controllable multi-camera driving scene generation

    Jiachen Lu, Ze Huang, Zeyu Yang, Jiahui Zhang, and Li Zhang. WoVoGen: World volume-aware diffusion for controllable multi-camera driving scene generation. InEuropean Conference on Computer Vision, pages 329–345. Springer, 2024

    2024

  60. [68]

    Controllable video generation: A survey.arXiv preprint arXiv:2507.16869, 2025

    Yue Ma, Kunyu Feng, Zhongyuan Hu, Xinyu Wang, Yucheng Wang, Mingzhe Zheng, Xuanhua He, Chenyang Zhu, Hongyu Liu, Yingqing He, Zeyu Wang, Zhifeng Li, Xiu Li, Wei Liu, Dan Xu, Linfeng Zhang, and Qifeng Chen. Controllable video generation: A survey.arXiv preprint arXiv:2507.16869, 2025

    2025

  61. [69]

    DreamForge: Motion-aware autoregressive video generation for multi-view driving scenes.arXiv preprint arXiv:2409.04003, 2024

    Jianbiao Mei, Tao Hu, Xuemeng Yang, Licheng Wen, Yu Yang, Tiantian Wei, Yukai Ma, Min Dou, Botian Shi, and Yong Liu. DreamForge: Motion-aware autoregressive video generation for multi-view driving scenes.arXiv preprint arXiv:2409.04003, 2024

    2024 arXiv

  62. [70]

    Vision-centric 4d occupancy forecasting and planning via implicit residual world models.arXiv preprint arXiv:2510.16729, 2025

    Jianbiao Mei, Yu Yang, Xuemeng Yang, Licheng Wen, Jiajun Lv, Botian Shi, and Yong Liu. Vision-centric 4d occupancy forecasting and planning via implicit residual world models.arXiv preprint arXiv:2510.16729, 2025

    2025

  63. [71]

    NeRF: Representing scenes as neural radiance fields for view synthesis.Communications of the ACM, 65(1): 99–106, 2021

    Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. NeRF: Representing scenes as neural radiance fields for view synthesis.Communications of the ACM, 65(1): 99–106, 2021

    2021

  64. [72]

    MaskGWM: A generalizable driving world model with video mask reconstruction

    Jingcheng Ni, Yuxin Guo, Yichen Liu, Rui Chen, Lewei Lu, and Zehuan Wu. MaskGWM: A generalizable driving world model with video mask reconstruction. InIEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22381–22391, 2025

    2025

  65. [73]

    Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles

    On-Road Automated Driving (ORAD) Committee. Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles. https://www.sae.org/standards/j3016_ 202104-taxonomy-definitions-terms-related-driving-automation-systems-road-motor-vehicles, 2021

    2021

  66. [74]

    OpenAI. Sora. Accessed February 15, 2024 [Online]https://sora.com/library, 2024. URLhttps://sora.com/ library

    2024

  67. [75]

    DINOv2: Learning robust visual features without supervision.Transactions on Machine Learning Research Journal, 2024

    Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Mahmoud Assran, Nicolas Ballas, Wojciech Galuba, Russell Howes, Po-Yao Huang, Shang-Wen Li, Ishan Misra, Michael Rabba...

    2024

  68. [76]

    A new pedestrian dataset for supervised learning

    Gary Overett, Lars Petersson, Nathan Brewer, Lars Andersson, and Niklas Pettersson. A new pedestrian dataset for supervised learning. InIEEE Intelligent Vehicles Symposium, pages 373–378, 2008

    2008

  69. [77]

    Genie 2: A large-scale foundation world model, 2024

    Jack Parker-Holder, Philip Ball, Jake Bruce, Vibhavari Dasagi, Kristian Holsheimer, Christos Kaplanis, Alexandre Moufarek, Guy Scully, Jeremy Shar, Jimmy Shi, Stephen Spencer, Jessica Yung, Michael Dennis, Sultan Kenjeyev, Shangbang Long, Vlad Mnih, Harris Chan, Maxime Gazeau,...

    2024

  70. [78]

    Four principles for physically interpretable world models.arXiv preprint arXiv:2503.02143, 2025

    Jordan Peper, Zhenjiang Mao, Yuang Geng, Siyuan Pan, and Ivan Ruchkin. Four principles for physically interpretable world models.arXiv preprint arXiv:2503.02143, 2025

    2025 arXiv

  71. [79]

    Learning transferable visual 76 models from natural language supervision

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. Learning transferable visual 76 models from natural language supervision. InInternational C...

    2021

  72. [80]

    SAM 2: Segment anything in images and videos

    Nikhila Ravi, Valentin Gabeur, Yuan-Ting Hu, Ronghang Hu, Chaitanya Ryali, Tengyu Ma, Haitham Khedr, Roman Rädle, Chloe Rolland, Laura Gustafson, Eric Mintun, Junting Pan, Kalyan Vasudev Alwala, Nicolas Carion, Chao-Yuan Wu, Ross Girshick, Piotr Dollár, and Christoph Feichtenh...

    2025

  73. [81]

    Grounded SAM: Assembling open-world models for diverse visual tasks.arXiv preprint arXiv:2401.14159, 2024

    Tianhe Ren, Shilong Liu, Ailing Zeng, Jing Lin, Kunchang Li, He Cao, Jiayu Chen, Xinyu Huang, Yukang Chen, Feng Yan, Zhaoyang Zeng, Hao Zhang, Feng Li, Jie Yang, Hongyang Li, Qing Jiang, and Lei Zhang. Grounded SAM: Assembling open-world models for diverse visual tasks.arXiv p...

    2024 arXiv

  74. [82]

    Cosmos-Drive-Dreams: Scalable synthetic driving data generation with world foundation models.arXiv preprint arXiv:2506.09042, 2025

    Xuanchi Ren, Yifan Lu, Tianshi Cao, Ruiyuan Gao, Shengyu Huang, Amirmojtaba Sabour, Tianchang Shen, Tobias Pfaff, Jay Zhangjie Wu, Runjian Chen, Seung Wook Kim, Jun Gao, Laura Leal-Taixe, Mike Chen, Sanja Fidler, and Huan Ling. Cosmos-Drive-Dreams: Scalable synthetic driving d...

    2025 arXiv

  75. [83]

    Gen3C: 3D-informed world-consistent video generation with precise camera control

    Xuanchi Ren, Tianchang Shen, Jiahui Huang, Huan Ling, Yifan Lu, Merlin Nimier-David, Thomas Müller, Alexander Keller, Sanja Fidler, and Jun Gao. Gen3C: 3D-informed world-consistent video generation with precise camera control. InIEEE/CVF Conference on Computer Vision and Patte...

    2025

  76. [84]

    VideoWorld: Exploring knowledge learning from unlabeled videos

    Zhongwei Ren, Yunchao Wei, Xun Guo, Yao Zhao, Bingyi Kang, Jiashi Feng, and Xiaojie Jin. VideoWorld: Exploring knowledge learning from unlabeled videos. InIEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 29029–29039, 2025

    2025

  77. [85]

    High-resolution image synthesis with latent diffusion models

    Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. InIEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10684–10695, 2022

    2022

  78. [86]

    GAIA-2: A controllable multi-view generative world model for autonomous driving.arXiv preprint arXiv:2503.20523, 2025

    Lloyd Russell, Anthony Hu, Lorenzo Bertoni, George Fedoseev, Jamie Shotton, Elahe Arani, and Gianluca Corrado. GAIA-2: A controllable multi-view generative world model for autonomous driving.arXiv preprint arXiv:2503.20523, 2025

    2025 arXiv

  79. [87]

    Photorealistic text-to-image diffusion models with deep language understanding.Advances in Neural Information Processing Systems, 35:36479–36494, 2022

    Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, Jonathan Ho, David J Fleet, and Mohammad Norouzi. Photorealistic text-to-image diffusion models with deep language un...

    2022

  80. [88]

    Improved techniques for training GANs.Advances in Neural Information Processing Systems, 29:2234–2242, 2016

    Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. Improved techniques for training GANs.Advances in Neural Information Processing Systems, 29:2234–2242, 2016

    2016

  81. [89]

    Open Driving World Models (OpenDWM).https://github.com/SenseTime-FVG/OpenDWM, 2025

    SenseTime-FVG. Open Driving World Models (OpenDWM).https://github.com/SenseTime-FVG/OpenDWM, 2025

    2025

  82. [90]

    RepVideo: Rethinking cross-layer representation for video generation.arXiv preprint arXiv:2501.08994, 2025

    Chenyang Si, Weichen Fan, Zhengyao Lv, Ziqi Huang, Yu Qiao, and Ziwei Liu. RepVideo: Rethinking cross-layer representation for video generation.arXiv preprint arXiv:2501.08994, 2025

    2025 arXiv

  83. [91]

    Make-a-video: Text-to-video generation without text-video data

    Uriel Singer, Adam Polyak, Thomas Hayes, Xi Yin, Jie An, Songyang Zhang, Qiyuan Hu, Harry Yang, Oron Ashual, Oran Gafni, Devi Parikh, Sonal Gupta, and Yaniv Taigman. Make-a-video: Text-to-video generation without text-video data. InIEEE/CVF Conference on Computer Vision and Pa...

    2024

  84. [92]

    LoFTR: Detector-free local feature matching with transformers

    Jiaming Sun, Zehong Shen, Yuang Wang, Hujun Bao, and Xiaowei Zhou. LoFTR: Detector-free local feature matching with transformers. InIEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8922–8931, 2021

    2021

  85. [93]

    T2V-CompBench: A comprehensive benchmark for compositional text-to-video generation

    Kaiyue Sun, Kaiyi Huang, Xian Liu, Yue Wu, Zihan Xu, Zhenguo Li, and Xihui Liu. T2V-CompBench: A comprehensive benchmark for compositional text-to-video generation. InIEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8406–8416, 2025

    2025

  86. [94]

    See clearer at night: towards robust nighttime semantic segmentation through day-night image conversion

    Lei Sun, Kaiwei Wang, Kailun Yang, and Kaite Xiang. See clearer at night: towards robust nighttime semantic segmentation through day-night image conversion. InArtificial Intelligence and Machine Learning in Defense Applications, volume 11169, pages 77–89. SPIE, 2019

    2019

  87. [95]

    Going deeper with convolutions

    Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. Going deeper with convolutions. InIEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1–9, 2015. 77

    2015

  88. [96]

    SparseOCC: Rethinking sparse latent representation for vision-based semantic occupancy prediction

    Pin Tang, Zhongdao Wang, Guoqing Wang, Jilai Zheng, Xiangxuan Ren, Bailan Feng, and Chao Ma. SparseOCC: Rethinking sparse latent representation for vision-based semantic occupancy prediction. InIEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15035–15044, 2024

    2024

  89. [97]

    Google Team. Veo2. Accessed December 18, 2024 [Online]https://deepmind.google/technologies/veo/veo-2/,

    2024

  90. [98]

    Kuaishou Team. Kling. Accessed December 9, 2024 [Online] https://klingai.kuaishou.com/, 2024. URL https://klingai.kuaishou.com/

    2024

  91. [99]

    HunyuanVideo: A systematic framework for large video generative models, 2024

    Tecent Team. HunyuanVideo: A systematic framework for large video generative models, 2024

    2024

  92. [100]

    Wan: Open and advanced large-scale video generative models, 2025

    Wan Team. Wan: Open and advanced large-scale video generative models, 2025

    2025

  93. [101]

    Towards accurate generative models of video: A new metric & challenges.arXiv preprint arXiv:1812.01717, 2018

    Thomas Unterthiner, Sjoerd Van Steenkiste, Karol Kurach, Raphael Marinier, Marcin Michalski, and Sylvain Gelly. Towards accurate generative models of video: A new metric & challenges.arXiv preprint arXiv:1812.01717, 2018

    2018 arXiv

  94. [102]

    AIGVQA: A unified framework for multi-dimensional quality assessment of AI-generated video

    Jiarui Wang, Juntong Wang, Xiaorong Zhu, Huiyu Duan, Guangtao Zhai, and Xiongkuo Min. AIGVQA: A unified framework for multi-dimensional quality assessment of AI-generated video. InIEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 93383–3390, 2025

    2025

  95. [103]

    ProphetDWM: A driving world model for rolling out future actions and videos

    Xiaodong Wang and Peixi Peng. ProphetDWM: A driving world model for rolling out future actions and videos. arXiv preprint arXiv:2505.18650, 2025

    2025 arXiv

  96. [104]

    LongDWM: Cross-granularity distillation for building a long-term driving world model.arXiv preprint arXiv:2506.01546, 2025

    Xiaodong Wang, Zhirong Wu, and Peixi Peng. LongDWM: Cross-granularity distillation for building a long-term driving world model.arXiv preprint arXiv:2506.01546, 2025

    2025 arXiv

  97. [105]

    DriveDreamer: Towards real-world-drive world models for autonomous driving

    Xiaofeng Wang, Zheng Zhu, Guan Huang, Xinze Chen, Jiagang Zhu, and Jiwen Lu. DriveDreamer: Towards real-world-drive world models for autonomous driving. InEuropean Conference on Computer Vision, pages 55–72. Springer, 2024

    2024

  98. [106]

    Lavie: High-quality video generation with cascaded latent diffusion models.International Journal of Computer Vision, 133(5):3059–3078, 2025

    Yaohui Wang, Xinyuan Chen, Xin Ma, Shangchen Zhou, Ziqi Huang, Yi Wang, Ceyuan Yang, Yinan He, Jiashuo Yu, Peiqing Yang, Yuwei Guo, Tianxing Wu, Chenyang Si, Yuming Jiang, Cunjian Chen, Chen Change Loy, Bo Dai, Dahua Lin, Yu Qiao, and Ziwei Liu. Lavie: High-quality video gener...

    2025

  99. [107]

    Yuping Wang, Shuo Xing, Cui Can, Renjie Li, Hongyuan Hua, Kexin Tian, Zhaobin Mo, Xiangbo Gao, Keshu Wu, Sulong Zhou, Hengxu You, Juntong Peng, Junge Zhang, Zehao Wang, Rui Song, Mingxuan Yan, Walter Zimmer, Xingcheng Zhou, Peiran Li, Zhaohan Lu, Chia-Ju Chen, Yue Huang, Ryan ...

    2025 arXiv

  100. [108]

    Driving into the future: Multiview visual forecasting and planning with world model for autonomous driving

    Yuqi Wang, Jiawei He, Lue Fan, Hongxin Li, Yuntao Chen, and Zhaoxiang Zhang. Driving into the future: Multiview visual forecasting and planning with world model for autonomous driving. InIEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14749–14759, 2024

    2024

  101. [109]

    Bovik, H.R

    Zhou Wang, A.C. Bovik, H.R. Sheikh, and E.P. Simoncelli. Image quality assessment: from error visibility to structural similarity.IEEE Transactions on Image Processing, 13(4):600–612, 2004

    2004

  102. [110]

    Person transfer gan to bridge domain gap for person re-identification

    Longhui Wei, Shiliang Zhang, Wen Gao, and Qi Tian. Person transfer gan to bridge domain gap for person re-identification. InIEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 79–88, 2018

    2018

  103. [111]

    LimSim: A long-term interactive multi-scenario traffic simulator

    Licheng Wen, Daocheng Fu, Song Mao, Pinlong Cai, Min Dou, Yikang Li, and Yu Qiao. LimSim: A long-term interactive multi-scenario traffic simulator. InIEEE International Conference on Intelligent Transportation Systems, pages 1255–1262, 2023

    2023

  104. [112]

    Panacea: Panoramic and controllable video generation for autonomous driving

    Yuqing Wen, Yucheng Zhao, Yingfei Liu, Fan Jia, Yanhui Wang, Chong Luo, Chi Zhang, Tiancai Wang, Xiaoyan Sun, and Xiangyu Zhang. Panacea: Panoramic and controllable video generation for autonomous driving. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, page...

    2024

  105. [113]

    Visual transformers: Token-based image representation and processing for computer vision.arXiv preprint arXiv:2006.03677, 2020

    Bichen Wu, Chenfeng Xu, Xiaoliang Dai, Alvin Wan, Peizhao Zhang, Zhicheng Yan, Masayoshi Tomizuka, Joseph Gonzalez, Kurt Keutzer, and Peter Vajda. Visual transformers: Token-based image representation and processing for computer vision.arXiv preprint arXiv:2006.03677, 2020. 78

    2006 arXiv

  106. [114]

    Tune-a-video: One-shot tuning of image diffusion models for text-to-video generation

    Jay Zhangjie Wu, Yixiao Ge, Xintao Wang, Stan Weixian Lei, Yuchao Gu, Yufei Shi, Wynne Hsu, Ying Shan, Xiaohu Qie, and Mike Zheng Shou. Tune-a-video: One-shot tuning of image diffusion models for text-to-video generation. InIEEE/CVF International Conference on Computer Vision,...

    2023

  107. [115]

    DriveScape: Towards high-resolution controllable multi-view driving video generation

    Wei Wu, Xi Guo, Weixuan Tang, Tingxuan Huang, Chiyu Wang, and Chenjing Ding. DriveScape: Towards high-resolution controllable multi-view driving video generation. InIEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17187–17196, 2025

    2025

  108. [116]

    Generating multimodal driving scenes via next-scene prediction

    Yanhao Wu, Haoyang Zhang, Tianwei Lin, Lichao Huang, Shujie Luo, Rui Wu, Congpei Qiu, Wei Ke, and Tong Zhang. Generating multimodal driving scenes via next-scene prediction. InIEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6844–6853, 2025

    2025

  109. [117]

    Alvarez, and Ping Luo

    Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, and Ping Luo. SegFormer: Simple and efficient design for semantic segmentation with transformers. InAdvances in Neural Information Processing Systems, volume 34, pages 12077–12090, 2021

    2021

  110. [118]

    Are VLMs ready for autonomous driving? an empirical study from the reliability, data, and metric perspectives

    Shaoyuan Xie, Lingdong Kong, Yuhao Dong, Chonghao Sima, Wenwei Zhang, Qi Alfred Chen, Ziwei Liu, and Liang Pan. Are VLMs ready for autonomous driving? an empirical study from the reliability, data, and metric perspectives. InIEEE/CVF International Conference on Computer Vision...

    2025

  111. [119]

    Human motion video generation: A survey.IEEE Transactions on Pattern Analysis and Machine Intelligence, 47(11):10709–10730, 2025

    Haiwei Xue, Xiangyang Luo, Zhanghao Hu, Xin Zhang, Xunzhi Xiang, Yuqin Dai, Jianzhuang Liu, Zhensong Zhang, Minglei Li, Jian Yang, Fei Ma, Zhiyong Wu, Changpeng Yang, Zonghong Dai, and Fei Richard Yu. Human motion video generation: A survey.IEEE Transactions on Pattern Analysi...

    2025

  112. [120]

    RLGF: Reinforcement learning with geometric feedback for autonomous driving video generation

    Tianyi Yan, Wencheng Han, Xia Zhou, Xueyang Zhang, Kun Zhan, Cheng-Zhong Xu, and Jianbing Shen. RLGF: Reinforcement learning with geometric feedback for autonomous driving video generation. InAdvances in Neural Information Processing Systems, volume 38, 2025

    2025

  113. [121]

    DrivingSphere: Building a high-fidelity 4D world for closed-loop simulation

    Tianyi Yan, Dongming Wu, Wencheng Han, Junpeng Jiang, Xia Zhou, Kun Zhan, Cheng zhong Xu, and Jianbing Shen. DrivingSphere: Building a high-fidelity 4D world for closed-loop simulation. InIEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 27531–27541, 2025

    2025

  114. [122]

    VideoGPT: Video generation using VQ-VAE and transformers.arXiv preprint arXiv:2104.10157, 2021

    Wilson Yan, Yunzhi Zhang, Pieter Abbeel, and Aravind Srinivas. VideoGPT: Video generation using VQ-VAE and transformers.arXiv preprint arXiv:2104.10157, 2021

    2021 arXiv

  115. [123]

    Street gaussians: Modeling dynamic urban scenes with gaussian splatting

    Yunzhi Yan, Haotong Lin, Chenxu Zhou, Weijie Wang, Haiyang Sun, Kun Zhan, Xianpeng Lang, Xiaowei Zhou, and Sida Peng. Street gaussians: Modeling dynamic urban scenes with gaussian splatting. InEuropean Conference on Computer Vision, pages 156–173. Springer, 2024

    2024

  116. [124]

    Generalized predictive model for autonomous driving

    Jiazhi Yang, Shenyuan Gao, Yihang Qiu, Li Chen, Tianyu Li, Bo Dai, Kashyap Chitta, Penghao Wu, Jia Zeng, Ping Luo, Jun Zhang, Andreas Geiger, Yu Qiao, and Hongyang Li. Generalized predictive model for autonomous driving. InIEEE/CVF Conference on Computer Vision and Pattern Rec...

    2024

  117. [125]

    BEVControl: Accurately controlling street-view elements with multi-perspective consistency via BEV sketch layout.arXiv preprint arXiv:2308.01661, 2023

    Kairui Yang, Enhui Ma, Jibin Peng, Qing Guo, Di Lin, and Kaicheng Yu. BEVControl: Accurately controlling street-view elements with multi-perspective consistency via BEV sketch layout.arXiv preprint arXiv:2308.01661, 2023

    2023 arXiv

  118. [126]

    Depth anything v2

    Lihe Yang, Bingyi Kang, Zilong Huang, Zhen Zhao, Xiaogang Xu, Jiashi Feng, and Hengshuang Zhao. Depth anything v2. InAdvances in Neural Information Processing Systems, volume 37, pages 21875–21911, 2024

    2024

  119. [127]

    DriveArena: A closed-loop generative simulation platform for autonomous driving

    Xuemeng Yang, Licheng Wen, Yukai Ma, Jianbiao Mei, Xin Li, Tiantian Wei, Wenjie Lei, Daocheng Fu, Pinlong Cai, Min Dou, Botian Shi, Liang He, Yong Liu, and Yu Qiao. DriveArena: A closed-loop generative simulation platform for autonomous driving. InIEEE/CVF International Confer...

    2025

  120. [128]

    X-Scene: Large-scale driving scene generation with high fidelity and flexible controllability

    Yu Yang, Alan Liang, Jianbiao Mei, Yukai Ma, Yong Liu, and Gim Hee Lee. X-Scene: Large-scale driving scene generation with high fidelity and flexible controllability. InAdvances in Neural Information Processing Systems, volume 38, 2025

    2025

  121. [129]

    Driving in the occupancy world: Vision-centric 4d occupancy forecasting and planning via world models for autonomous driving

    Yu Yang, Jianbiao Mei, Yukai Ma, Siliang Du, Wenqing Chen, Yijie Qian, Yuxiang Feng, and Yong Liu. Driving in the occupancy world: Vision-centric 4d occupancy forecasting and planning via world models for autonomous driving. InProceedings of the AAAI Conference on Artificial I...

    2025

  122. [130]

    DualDiff+: Dual-branch diffusion for high-fidelity video generation with reward guidance.arXiv preprint arXiv:2503.03689, 2025

    Zhao Yang, Zezhong Qian, Xiaofan Li, Weixiang Xu, Gongpeng Zhao, Ruohong Yu, Lingsi Zhu, and Longjun Liu. DualDiff+: Dual-branch diffusion for high-fidelity video generation with reward guidance.arXiv preprint arXiv:2503.03689, 2025. 79

    2025 arXiv

  123. [131]

    Physical informed driving world model

    Zhuoran Yang, Xi Guo, Chenjing Ding, Chiyu Wang, and Wei Wu. Physical informed driving world model. arXiv preprint arXiv:2412.08410, 2024

    2024 arXiv

  124. [132]

    CogVideoX: Text-to-video diffusion models with an expert transformer

    Zhuoyi Yang, Jiayan Teng, Wendi Zheng, Ming Ding, Shiyu Huang, Jiazheng Xu, Yuanming Yang, Wenyi Hong, Xiaohan Zhang, Guanyu Feng, et al. CogVideoX: Text-to-video diffusion models with an expert transformer. arXiv preprint arXiv:2408.06072, 2024

    2024 arXiv

  125. [133]

    Oasis: Open agent social interaction simulations with one million agents.arXiv preprint arXiv:2411.11581, 2024

    Ziyi Yang, Zaibin Zhang, Zirui Zheng, Yuxian Jiang, Ziyue Gan, Zhiyu Wang, Zijian Ling, Jinsong Chen, Martz Ma, Bowen Dong, Prateek Gupta, Shuyue Hu, Zhenfei Yin, Guohao Li, Xu Jia, Lijun Wang, Bernard Ghanem, Huchuan Lu, Chaochao Lu, Wanli Ouyang, Yu Qiao, Philip Torr, and Ji...

    2024 arXiv

  126. [134]

    GSplat: An open-source library for Gaussian splatting

    Vickie Ye, Ruilong Li, Justin Kerr, Matias Turkulainen, Brent Yi, Zhuoyang Pan, Otto Seiskari, Jianbo Ye, Jeffrey Hu, Matthew Tancik, and Angjoo Kanazawa. GSplat: An open-source library for Gaussian splatting. Journal of Machine Learning Research, 26(34):1–17, 2025

    2025

  127. [135]

    WonderWorld: Interactive 3D scene generation from a single image

    Hong-Xing Yu, Haoyi Duan, Charles Herrmann, William T Freeman, and Jiajun Wu. WonderWorld: Interactive 3D scene generation from a single image. InIEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5916–5926, 2025

    2025

  128. [136]

    Simulating the world model with artificial intelligence: A roadmap.arXiv preprint arXiv:2511.08585, 2025

    Jingtong Yue, Ziqi Huang, Zhaoxi Chen, Xintao Wang, Pengfei Wan, and Ziwei Liu. Simulating the world model with artificial intelligence: A roadmap.arXiv preprint arXiv:2511.08585, 2025

    2025

  129. [137]

    Evaluation agent: Efficient and promptable evaluation framework for visual generative models

    Fan Zhang, Shulin Tian, Ziqi Huang, Yu Qiao, and Ziwei Liu. Evaluation agent: Efficient and promptable evaluation framework for visual generative models. InAnnual Meeting of the Association for Computational Linguistics, 2024

    2024

  130. [138]

    A simple framework for open-vocabulary segmentation and detection

    Hao Zhang, Feng Li, Xueyan Zou, Shilong Liu, Chunyuan Li, Jianwei Yang, and Lei Zhang. A simple framework for open-vocabulary segmentation and detection. InIEEE/CVF International Conference on Computer Vision, pages 1020–1031, 2023

    2023

  131. [139]

    Epona: Autoregressive diffusion world model for autonomous driving

    Kaiwen Zhang, Zhenyu Tang, Xiaotao Hu, Xingang Pan, Xiaoyang Guo, Yuan Liu, Jingwei Huang, Li Yuan, Qian Zhang, Xiao-Xiao Long, Xun Cao, and Wei Yin. Epona: Autoregressive diffusion world model for autonomous driving. InIEEE/CVF International Conference on Computer Vision, pag...

    2025

  132. [140]

    Efros, Eli Shechtman, and Oliver Wang

    Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. InIEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 586–595, 2018

    2018

  133. [141]

    A perspective on quality evaluation for AI-generated videos

    Zhichao Zhang, Wei Sun, and Guangtao Zhai. A perspective on quality evaluation for AI-generated videos. Sensors, 25:4668, 2025

    2025

  134. [142]

    DriveDreamer-2: LLM-enhanced world models for diverse driving video generation

    Guosheng Zhao, Xiaofeng Wang, Zheng Zhu, Xinze Chen, Guan Huang, Xiaoyi Bao, and Xingang Wang. DriveDreamer-2: LLM-enhanced world models for diverse driving video generation. InAAAI Conference on Artificial Intelligence, volume 39, pages 10412–10420, 2025

    2025

  135. [143]

    VBench-2.0: Advancing video generation benchmark suite for intrinsic faithfulness.arXiv preprint arXiv:2503.21755, 2025

    Dian Zheng, Ziqi Huang, Hongbo Liu, Kai Zou, Yinan He, Fan Zhang, Lulu Gu, Yuanhan Zhang, Jingwen He, Wei-Shi Zheng, Yu Qiao, and Ziwei Liu. VBench-2.0: Advancing video generation benchmark suite for intrinsic faithfulness.arXiv preprint arXiv:2503.21755, 2025

    2025 arXiv

  136. [144]

    Scalable person re-identification: A benchmark

    Liang Zheng, Liyue Shen, Lu Tian, Shengjin Wang, Jingdong Wang, and Qi Tian. Scalable person re-identification: A benchmark. InIEEE/CVF International Conference on Computer Vision, pages 1116–1124, 2015

    2015

  137. [145]

    HERMES: A unified self-driving world model for simultaneous 3D scene understanding and generation

    Xin Zhou, Dingkang Liang, Sifan Tu, Xiwu Chen, Yikang Ding, Dingyuan Zhang, Feiyang Tan, Hengshuang Zhao, and Xiang Bai. HERMES: A unified self-driving world model for simultaneous 3D scene understanding and generation. InIEEE/CVF International Conference on Computer Vision, p...

    2025

  138. [146]

    SimGen: Simulator-conditioned driving scene generation

    Yunsong Zhou, Michael Simon, Zhenghao Mark Peng, Sicheng Mo, Hongzi Zhu, Minyi Guo, and Bolei Zhou. SimGen: Simulator-conditioned driving scene generation. InAdvances in Neural Information Processing Systems, volume 37, pages 48838–48874, 2024

    2024

  139. [147]

    SPIRAL: Semantic-aware progressive LiDAR scene generation

    Dekai Zhu, Yixuan Hu, Youquan Liu, Dongyue Lu, Lingdong Kong, and Slobodan Ilic. SPIRAL: Semantic-aware progressive LiDAR scene generation. InAdvances in Neural Information Processing Systems, volume 38, 2025

    2025

  140. [148]

    Cross-video identity correlating for person re-identification pre-training.Advances in Neural Information Processing Systems, 37:25228–25250, 2024

    Jialong Zuo, Ying Nie, Hanyu Zhou, Huaxin Zhang, Haoyu Wang, Tianyu Guo, Nong Sang, and Changxin Gao. Cross-video identity correlating for person re-identification pre-training.Advances in Neural Information Processing Systems, 37:25228–25250, 2024. 80

    2024

  141. [2025]

    URLhttps://deepmind.google/technologies/veo/veo-2/

Pith tools

Reviewed August 3, 2026 · model on record in the stance chip above.