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REVIEW 5 major objections 5 minor 7 cited by

EWMBench: Evaluating Scene, Motion, and Semantic Quality in Embodied World Models

T0 review · 5 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read EWMBench ranks embodied video models closer to human judgments than VBench does.

desk verdict Useful early benchmark for embodied world models, but the human-alignment claim outruns the evidence and the metric aggregation needs a principled rework before the scores are used as ground truth. read the letter →

arxiv 2505.09694 v2 pith:O6BF2CAS submitted 2025-05-14 cs.RO

Hu Yue , Siyuan Huang , Yue Liao , Shengcong Chen , Pengfei Zhou , Liliang Chen , Maoqing Yao , Guanghui Ren This is my paper · ORCID
classification cs.RO
keywords embodiedworldmodelsvideogenerationevaluationroboticmanipulationtrajectorymetricssceneconsistencysemanticalignmenthumanbenchmarkdataset
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

This paper argues that judging embodied world models—video generators asked to produce the next frames of a robotic manipulation—requires metrics for visual scene consistency, motion correctness, and semantic alignment, not just general video quality or human preference. It introduces EWMBench, a benchmark built on real manipulation data, with 30 curated episodes across 10 tasks, automated trajectory and scene detectors, and a prompt suite that makes a video MLLM caption, break down steps, and penalize logic errors. The paper's central claim is that EWMBench's rankings agree with human annotators more closely than VBench's rankings do, and that domain-adapted models (EnerVerse_FT, LTX_FT) outperform commercial and open-source generators on these embodied criteria. If the claim holds, the benchmark gives embodied-AI developers a way to measure progress on physically grounded, action-consistent generation rather than on visual polish alone.

What carries the argument

The machinery has three parts. Fine-tuned YOLO-World detects the end-effector in every frame and BoT-SORT links detections into trajectories; the largest-spatial-extent hand's trajectory is compared with ground truth. The trajectory metrics are Symmetric Hausdorff Distance (maximum spatial deviation), Normalized Dynamic Time Warping (spatial-temporal alignment and task sequencing), and Dynamic Consistency (Wasserstein distance between velocity and acceleration distributions, amplitude-normalized). Scene consistency uses a DINOv2 ViT-B/14 fine-tuned for 20,000 iterations on embodied data, computing cosine similarity between patch embeddings of initial and consecutive frames. Semantic evaluation uses a video MLLM with three prompt levels—global caption compared by BLEU to the instruction, step descriptions compared by CLIP to ground-truth sub-action captions, and logic-error penalties for hallucinations and impossible spatial relations. The benchmark protocol takes initial frames, language instruction, and an optional action trajectory, normalizes outputs to 640x480 at 30 FPS, and requires three generated videos per episode with best-of-three selection.

What would settle it

Compute EWMBench and human rankings on a held-out set of generated videos that contain human hands instead of robot arms. If the benchmark ranks models higher than humans do on those cases, the detector assumption fails and the human-alignment claim does not generalize.

Watch

Extended reading notes

Core claim

On its own terms, the paper establishes EWMBench as a benchmark that scores generated manipulation videos along three axes: scene consistency (static layout, object permanence, viewpoint coherence via fine-tuned DINOv2 cosine similarity), motion correctness (end-effector trajectory compared to ground truth with Hausdorff distance, normalized dynamic time warping, and Wasserstein-based dynamic consistency), and semantic alignment (global captions matched by BLEU, step descriptions by CLIP, plus explicit logic-error penalties). In the paper's experiments, EWMBench ranks the domain-adapted EnerVerse_FT and LTX_FT above commercial models like Kling and Hailuo and open-source models like COSMOS, OpenSora, and LTX, with EnerVerse_FT and Kling showing the strongest semantic alignment. The paper reports that in a human evaluation over four representative models, EWMBench's ranking matched the aggregated human ranking more closely than VBench's ranking did. It also shows that the three trajectory metrics respond differently to sequence reversal, outlier insertion, and frame repetition, which the paper reads as evidence that all three are needed.

Load-bearing premise

The end-effector tracker and the scene-consistency feature extractor, both fine-tuned on the Agibot-World dataset, reliably measure the true task-relevant motion and scene structure in videos that those models were not trained on, including generated frames that show human hands, cartoon-like scenes, or drifting viewpoints.

Editorial extensions

If this is right

  • The benchmark's ranking of domain-adapted models above commercial and open-source ones supports the paper's conclusion that domain-specific fine-tuning improves embodied generation quality.
  • Because the trajectory metrics diverge on reversed, outlier, and repeated sequences, measuring embodied motion requires all three rather than any single distance.
  • EWMBench's closer agreement with human rankings over VBench indicates that general benchmarks' foreground-background entanglement is a measurable limitation for embodied evaluation.
  • The benchmark currently restricts itself to end-effector trajectories and fixed viewpoints, scoping its conclusions to that setting.

Reading between the lines

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

  • A natural extension, not pursued in the paper, would be to run the same human study on all seven models, not just four; if the ranking agreement persists, the case for EWMBench over VBench strengthens.
  • Because EWMBench's motion scores depend on detecting a single end-effector, models that render human hands instead of robot arms could receive inflated trajectory scores if the detector tracks the hand; a targeted audit of hand-vs-arm cases would clarify whether the metric measures task correctness or merely object tracking.
  • The logic-error penalty in the MLLM prompt suite could be turned into a standalone audit protocol for physical-commonsense violations in generated manipulation videos, independent of trajectory extraction.
  • The trajectory-diversity sampling with voxelized IoU and greedy selection suggests a recipe for building harder evaluation splits: choose episodes whose ground-truth trajectories are least similar, which may better expose models that only reproduce average motion.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

5 major / 5 minor

Summary. EWMBench is a proposed benchmark for embodied world models (EWMs), evaluating image/text-conditioned video generation on three dimensions: visual scene consistency, motion correctness (via end-effector trajectory metrics HSD, nDTW, and DYN), and semantic alignment (via MLLM captioning, CLIP scores, and logic-error penalties). The dataset is curated from Agibot-World across ten manipulation tasks with diversity-based trajectory sampling, and seven video generation models are compared. The paper claims that EWMBench rankings align more closely with human judgments than VBench rankings (Section 4.2) and provides qualitative analyses of model failure modes. The dataset and evaluation tools are publicly available.

Significance. The benchmark addresses a genuine gap: existing video generation benchmarks focus on perceptual quality and lack embodied task grounding. The decomposition into scene/motion/semantics, the publicly released evaluation toolkit, and the controlled experiment showing complementary behavior of HSD, nDTW, and DYN (Figure 6C) are useful contributions. The qualitative failure-mode analysis (e.g., human-hand bias in COSMOS/LTX, viewpoint drift in COSMOS) is also informative. However, the central human-alignment claim and the reliability of the automated trajectory/scene detectors are not yet established; several methodological choices (best-of-three selection, additive overall score, arbitrary DYN weights) affect the benchmark's validity as currently presented.

major comments (5)
  1. [3.3, Table 2] Best-of-three selection is circular for HSD. In Section 3.3, the protocol generates three candidate trajectories per sample and selects 'the best trajectory... based on Hausdorff distance.' That same HSD value is then reported in Table 2. As a result, the reported HSD is the minimum over three draws of a distance (equivalently the maximum of the reciprocal score), which inflates scores and disproportionately benefits models with high generation variance. Because the Overall column in Table 2 includes HSD, the ranking is directly affected. Please report all candidates or, at minimum, use a selection criterion independent of the reported metric and justify the choice.
  2. [Table 2] The Overall score is an unweighted arithmetic sum of components with different ranges. SceneC contributes at most 1, the motion 'Avg' is actually the sum HSD+Dyn+nDTW (at most 3), and the semantic 'Avg' is the sum Diversity+BLEU+CLIP+Logics (at most 4). Thus the overall ranking implicitly weights semantics four times and motion three times relative to scene, and the labels 'Avg.' are misleading. For example, EnerVerse_FT's lead over Kling is driven substantially by the larger number of semantic sub-scores. Please report a stated aggregation rule (e.g., average within each dimension, then a declared weighted combination) and show sensitivity to the aggregation.
  3. [A.3.2, Eq. (7)] The DYN score in Eq. (7) depends on two constants alpha=0.007 and beta=0.003 with no stated justification. These weights determine the relative contribution of velocity and acceleration, and the resulting DYN magnitudes feed into the Overall score in Table 2. Since the paper argues that DYN captures a distinct failure mode, the constant choice should be supported (e.g., calibrated on a labeled set or reported with a sensitivity analysis) rather than asserted.
  4. [4.2, Figure 6] The human-alignment claim is not statistically supported. Section 4.2 reports no number of annotators, no number of rated videos, no per-item scores, and no inter-annotator agreement; it only states that 'multiple review rounds' ensured reliability. The comparison with VBench in Figure 6(B) lacks the per-model VBench scores and any test of whether the difference in ranking agreement is significant. With only four models, random variation in either the human or VBench ranking can change the conclusion. Please provide the full evaluation protocol, per-item data, and a significance test (e.g., Kendall's tau with bootstrap).
  5. [A.1, 3.3] The trajectory and scene metrics depend on detectors whose reliability on generated videos is unmeasured. Trajectory extraction uses a YOLO-World model fine-tuned on 1,451 annotated Agibot-World frames, and scene consistency uses DINOv2 fine-tuned on Agibot-World. Generated videos can contain human hands instead of robot end-effectors (as the paper itself notes for COSMOS and LTX in Appendix A.4), cartoon-like scenes (Hailuo), or viewpoint drift (COSMOS). No detection accuracy is reported on generated frames, so HSD, nDTW, and DYN may be unreliable for exactly the models the benchmark is discriminating. A manual audit of trajectories on a sample of generated videos, or a report of detector confidence on the generated corpus, is needed.
minor comments (5)
  1. [1, 4, A.2.2] The number of benchmark episodes is inconsistent: the introduction says '30 candidate samples across ten tasks' while Section 4 says ten tasks with ten ground-truth episodes each, and Appendix A.2.2 says 100 instances per task with 10 selected. Please clarify the exact dataset size.
  2. [Table 2] In Table 2, the column headers are ambiguous: the 'Avg.' columns are sums, and the Overall column is a sum of sums; a clearer breakdown with per-dimension averages would aid reproducibility.
  3. [4.2] The scoring scheme assigns 3, 2, and 0 to best, second-best, and worst, with no value for third-best; please clarify whether this is deliberate and report the exact scoring rubric.
  4. [3.3, A.3.2] The global semantic alignment score uses BLEU to compare an MLLM-generated caption to the raw task instruction; BLEU is sensitive to lexical overlap and may be a poor measure for this open-ended comparison. Please report the actual BLEU values or motivate the choice.
  5. [A.1] Appendix A.1 reports precision/recall of the fine-tuned YOLO-World on held-out Agibot-World tasks, but not on generated videos; if detector performance on generated data is available, add it here.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the benchmark's metrics are defined against ground-truth trajectories and independent human rankings, not against the models' own outputs.

full rationale

EWMBENCH's evaluation chain is self-contained rather than circular. The visual scene, motion, and semantic metrics are defined with reference to ground-truth trajectories, initial frames, and task instructions that are external to the video generators being tested. The motion metrics (HSD, nDTW, DYN) compare detected end-effector trajectories to ground-truth trajectories from the curated dataset; no metric parameter is fitted from the generated videos and then renamed as a prediction. The best-of-three protocol ('The best trajectory is selected based on Hausdorff distance') is a transparent order-statistic selection rule, not a definitional reduction: the reported HSD is the minimum over three candidate outputs, which may inflate scores but does not make the benchmark's ranking equal to its inputs. The human-alignment claim in Section 4.2 is validated against independently aggregated human rankings; although the statistical reporting is thin, that is a robustness concern, not circularity. The fine-tuned YOLO-World and DINOv2 models are auxiliary detectors trained on Agibot-World, but they are not fitted parameters of the benchmark's scoring equations, and the benchmark's central derivation does not reduce to them. Self-citations to Agibot-World and EnerVerse are data/model references rather than load-bearing support for the benchmark's validity. No equation in the paper is shown to be equivalent to its own input by construction, so the appropriate finding is no significant circularity.

Assumptions & free parameters 1 free parameters · 5 assumptions · 0 invented entities

The paper introduces no new physical entities, forces, or conserved quantities. The benchmark is an artifact, not a postulated entity. The main free parameter is the hand-chosen DYN weighting; the axioms are the measurement assumptions that the benchmark's validity depends on.

free parameters (1)
  • DYN velocity and acceleration weights alpha, beta = alpha=0.007, beta=0.003
    Equation (7) assigns arbitrary weights to velocity and acceleration terms in the dynamic consistency score; no derivation or sensitivity analysis is given, and the resulting scale differs from other sub-scores.
assumptions (5)
  • domain assumption Human rankings are a valid ground truth for overall embodied video quality.
    Section 4.2 uses aggregated human rankings to validate the benchmark, but reports no inter-annotator agreement and only four models; the benchmark's validity rests on this assumption.
  • domain assumption Extracted 2D end-effector trajectories from fine-tuned YOLO-World plus BoT-SORT accurately capture task-relevant motion in generated videos.
    Appendix A.1 describes this pipeline; it is used for HSD, nDTW, and DYN. Generated videos may contain human hands, cartoon scenes, or drift, so detector reliability is not established beyond a recall of 0.9167 on two held-out tasks.
  • domain assumption Fine-tuned DINOv2 patch cosine similarity is a valid scene-consistency measure.
    Section 3.3.A uses DINOv2 fine-tuned on Agibot-World with unsupervised training; no quantitative validation of feature reliability is given beyond qualitative examples.
  • domain assumption The sampled 10 tasks and 10 episodes per task from Agibot-World represent embodied manipulation diversity.
    Section 3.2 and A.2 select tasks favoring action-ordering constraints and diverse trajectories, but representativeness is asserted, not measured.
  • domain assumption BLEU and CLIP scores computed between MLLM captions and instructions or GT captions are valid semantic alignment measures.
    Section 3.3.C uses BLEU on short single-reference captions and CLIP on step descriptions; no human validation is reported for the semantic sub-scores.

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Cite this review

Pith. "Pith review of EWMBench: Evaluating Scene, Motion, and Semantic Quality in Embodied World Models." pith.science (2026). https://pith.science/paper/O6BF2CAS

@misc{pith2026250509694,
  author       = {Pith},
  title        = {Pith review of: EWMBench: Evaluating Scene, Motion, and Semantic Quality in Embodied World Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/O6BF2CAS}},
  note         = {Machine review of arXiv:2505.09694}
}
read the original abstract

Recent advances in creative AI have enabled the synthesis of high-fidelity images and videos conditioned on language instructions. Building on these developments, text-to-video diffusion models have evolved into embodied world models (EWMs) capable of generating physically plausible scenes from language commands, effectively bridging vision and action in embodied AI applications. This work addresses the critical challenge of evaluating EWMs beyond general perceptual metrics to ensure the generation of physically grounded and action-consistent behaviors. We propose the Embodied World Model Benchmark (EWMBench), a dedicated framework designed to evaluate EWMs based on three key aspects: visual scene consistency, motion correctness, and semantic alignment. Our approach leverages a meticulously curated dataset encompassing diverse scenes and motion patterns, alongside a comprehensive multi-dimensional evaluation toolkit, to assess and compare candidate models. The proposed benchmark not only identifies the limitations of existing video generation models in meeting the unique requirements of embodied tasks but also provides valuable insights to guide future advancements in the field. The dataset and evaluation tools are publicly available at https://github.com/AgibotTech/EWMBench.

Figures

Figures reproduced from arXiv: 2505.09694 by the authors.

Figure 1
Figure 1. Comparison between general video generation and embodied video generation. Unlike [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Overview of the EWMBENCH benchmark design. The framework begins with unified world initialization, where generative models are instructed to produce predictive video frames based on initial scene images, task instructions, and optional action trajectories. These generated video frames are subsequently evaluated using multi-dimensional metrics, focusing on scene consistency, motion dynamics, and semantic alignment. 3… view at source ↗
Figure 3
Figure 3. Overview of the constructed dataset. Left: Task scenes spanning household, commercial, and industrial environments. Middle: Diverse task-specific trajectory variations within each scene. Right: Broad semantic coverage across various manipulation contexts. 3.3 Evaluation Metrics EWMBENCH systematically evaluates three dimensions to ensure the generated outputs are visually realistic, action plausible, and semanticall… view at source ↗
Figures from the paper (11 more)
Figure 4
Figure 4. Figure 4: Evaluation Results of Video Generative Models. [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Typical examples. EWMBENCH scores align well with scene and motion accuracy, demonstrating the interpretability and robustness of the proposed metrics. 4.2 Human Evaluation To evaluate the alignment between automated metrics and human judgment, we conducted a human eva…
Figure 6
Figure 6. Figure 6: (A) Aggregated human rankings of model predictions. (B) Comparison of rankings [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: Dataset overview. The first two rows display selected task scenarios and their associated action motion trajectories. The third row categorizes object properties (e.g., fluid, articulated, rigid, deformable, multi-body) using color-coded legends and representative exam…
Figure 8
Figure 8. Figure 8: Feature map comparison across models. DINOv2 fine-tuned on embodied data captures agents and tools with sharper spatial coherence, enabling more reliable scene stability evaluation. Scene Consistency: 0.6927 Background Consistency: 0.8845 [PITH_FULL_IMAGE:figures/full…
Figure 9
Figure 9. Figure 9: Scene Consistency failure cases. Despite camera movement and background drift, VBench assigns high scores (0.88–0.91). Our metric, however, detects the instability through decreased cosine similarity. the initialization. We visualize feature maps from three model varia…
Figure 10
Figure 10. Figure 10: Domain-adapted model failure case. The robot hand moves toward the correct region but fails to close the gripper on the object, resulting in empty grasping. LTX and COSMOS [PITH_FULL_IMAGE:figures/full_fig_p015_10.png]
Figure 11
Figure 11. Figure 11: Despite being given explicit camera viewpoint control instructions, COSMOS fails to [PITH_FULL_IMAGE:figures/full_fig_p016_11.png]
Figure 12
Figure 12. Figure 12: Examples illustrating the poor task understanding and temporal instability of the LTX [PITH_FULL_IMAGE:figures/full_fig_p016_12.png]
Figure 13
Figure 13. Figure 13: Generated videos from COSMOS and LTX models often depict human hands instead [PITH_FULL_IMAGE:figures/full_fig_p016_13.png]
Figure 14
Figure 14. Figure 14: An example from OpenSora illustrating unstable robotic arm motion. Although the scene [PITH_FULL_IMAGE:figures/full_fig_p017_14.png]

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Forward citations

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Reference graph

Works this paper leans on

43 extracted references · 14 canonical work pages · cited by 7 Pith papers

  1. [1]

    Cosmos world foundation model platform for physical ai.arXiv preprint arXiv:2501.03575, 2025

    Niket Agarwal, Arslan Ali, Maciej Bala, Yogesh Balaji, Erik Barker, Tiffany Cai, Prithvijit Chattopadhyay, Yongxin Chen, Yin Cui, Yifan Ding, et al. Cosmos world foundation model platform for physical ai.arXiv preprint arXiv:2501.03575, 2025

    arXiv 2025

  2. [2]

    Agibot world.https://agibot-world.com, 2024

    AgiBot. Agibot world.https://agibot-world.com, 2024

    work page 2024

  3. [3]

    Bot-sort: Robust associations multi-pedestrian tracking

    Nir Aharon, Roy Orfaig, and Ben-Zion Bobrovsky. Bot-sort: Robust associations multi-pedestrian tracking. arXiv preprint arXiv:2206.14651, 2022

    arXiv 2022

  4. [4]

    Vidu: a highly consistent, dynamic and skilled text-to-video generator with diffusion models.arXiv preprint arXiv:2405.04233, 2024

    Fan Bao, Chendong Xiang, Gang Yue, Guande He, Hongzhou Zhu, Kaiwen Zheng, Min Zhao, Shilong Liu, Yaole Wang, and Jun Zhu. Vidu: a highly consistent, dynamic and skilled text-to-video generator with diffusion models.arXiv preprint arXiv:2405.04233, 2024

    arXiv 2024

  5. [5]

    Stable video diffusion: Scaling latent video diffusion models to large datasets.arXiv preprint arXiv:2311.15127, 2023

    Andreas Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch, Maciej Kilian, Dominik Lorenz, Yam Levi, Zion English, Vikram V oleti, Adam Letts, et al. Stable video diffusion: Scaling latent video diffusion models to large datasets.arXiv preprint arXiv:2311.15127, 2023

    arXiv 2023

  6. [6]

    Video generation models as world simulators

    Tim Brooks, Bill Peebles, Connor Holmes, Will DePue, Yufei Guo, Li Jing, David Schnurr, Joe Taylor, Troy Luhman, Eric Luhman, et al. Video generation models as world simulators. 2024.URL https://openai. com/research/video-generation-models-as-world-simulators, 3:1, 2024

    work page 2024

  7. [7]

    Gr-2: A generative video-language-action model with web-scale knowledge for robot manipulation.arXiv preprint arXiv:2410.06158, 2024

    Chi-Lam Cheang, Guangzeng Chen, Ya Jing, Tao Kong, Hang Li, Yifeng Li, Yuxiao Liu, Hongtao Wu, Jiafeng Xu, Yichu Yang, et al. Gr-2: A generative video-language-action model with web-scale knowledge for robot manipulation.arXiv preprint arXiv:2410.06158, 2024

    arXiv

  8. [8]

    Videocrafter1: Open diffusion models for high-quality video generation, 2023

    Haoxin Chen, Menghan Xia, Yingqing He, Yong Zhang, Xiaodong Cun, Shaoshu Yang, Jinbo Xing, Yaofang Liu, Qifeng Chen, Xintao Wang, Chao Weng, and Ying Shan. Videocrafter1: Open diffusion models for high-quality video generation, 2023

    2023

Show all 43 references
  1. [9]

    Yolo-world: Real- time open-vocabulary object detection

    Tianheng Cheng, Lin Song, Yixiao Ge, Wenyu Liu, Xinggang Wang, and Ying Shan. Yolo-world: Real- time open-vocabulary object detection. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16901–16911, 2024

    2024

  2. [10]

    Eva: An embodied world model for future video anticipation.arXiv preprint arXiv:2410.15461, 2024

    Xiaowei Chi, Hengyuan Zhang, Chun-Kai Fan, Xingqun Qi, Rongyu Zhang, Anthony Chen, Chi-min Chan, Wei Xue, Wenhan Luo, Shanghang Zhang, et al. Eva: An embodied world model for future video anticipation.arXiv preprint arXiv:2410.15461, 2024

    arXiv

  3. [11]

    Tc-bench: Benchmarking temporal compositionality in text-to-video and image-to-video generation.arXiv preprint arXiv:2406.08656, 2024

    Weixi Feng, Jiachen Li, Michael Saxon, Tsu-jui Fu, Wenhu Chen, and William Yang Wang. Tc-bench: Benchmarking temporal compositionality in text-to-video and image-to-video generation.arXiv preprint arXiv:2406.08656, 2024

    2024 arXiv

  4. [12]

    Animatediff: Animate your personalized text-to-image diffusion models without specific tuning.arXiv preprint arXiv:2307.04725, 2023

    Yuwei Guo, Ceyuan Yang, Anyi Rao, Zhengyang Liang, Yaohui Wang, Yu Qiao, Maneesh Agrawala, Dahua Lin, and Bo Dai. Animatediff: Animate your personalized text-to-image diffusion models without specific tuning.arXiv preprint arXiv:2307.04725, 2023

    2023 arXiv

  5. [13]

    Ltx-video: Realtime video latent diffusion.arXiv preprint arXiv:2501.00103, 2024

    Yoav HaCohen, Nisan Chiprut, Benny Brazowski, Daniel Shalem, Dudu Moshe, Eitan Richardson, Eran Levin, Guy Shiran, Nir Zabari, Ori Gordon, et al. Ltx-video: Realtime video latent diffusion.arXiv preprint arXiv:2501.00103, 2024

    2024 arXiv

  6. [14]

    Hailuoai.https://hailuoai.video/, 2025

    Hailuo. Hailuoai.https://hailuoai.video/, 2025

    2025

  7. [15]

    GANs trained by a two time-scale update rule converge to a local nash equilibrium

    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. InAdvances in neural information processing systems, 2017

    2017

  8. [16]

    Denoising diffusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020

    Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020

    2020

  9. [17]

    Enerverse: Envisioning embodied future space for robotics manipulation.arXiv preprint arXiv:2501.01895, 2025

    Siyuan Huang, Liliang Chen, Pengfei Zhou, Shengcong Chen, Zhengkai Jiang, Yue Hu, Yue Liao, Peng Gao, Hongsheng Li, Maoqing Yao, et al. Enerverse: Envisioning embodied future space for robotics manipulation.arXiv preprint arXiv:2501.01895, 2025

    2025

  10. [18]

    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, et al. Vbench: Comprehensive benchmark suite for video generative models. InProceedings of the IEEE/CVF Conference on Computer Vision and Patt...

    2024

  11. [19]

    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

  12. [20]

    T2vbench: Benchmarking temporal dynamics for text-to-video generation

    Pengliang Ji, Chuyang Xiao, Huilin Tai, and Mingxiao Huo. T2vbench: Benchmarking temporal dynamics for text-to-video generation. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5325–5335, 2024

    2024

  13. [21]

    Hunyuanvideo: A systematic framework for large video generative models.arXiv preprint arXiv:2412.03603, 2024

    Weijie Kong, Qi Tian, Zijian Zhang, Rox Min, Zuozhuo Dai, Jin Zhou, Jiangfeng Xiong, Xin Li, Bo Wu, Jianwei Zhang, et al. Hunyuanvideo: A systematic framework for large video generative models.arXiv preprint arXiv:2412.03603, 2024

    2024 arXiv

  14. [22]

    Kling.https://app.klingai.com/cn/, 2025

    Kuaishou. Kling.https://app.klingai.com/cn/, 2025

    2025

  15. [23]

    Vmbench: A benchmark for perception-aligned video motion generation.arXiv preprint arXiv:2503.10076, 2025

    Xinrang Ling, Chen Zhu, Meiqi Wu, Hangyu Li, Xiaokun Feng, Cundian Yang, Aiming Hao, Jiashu Zhu, Jiahong Wu, and Xiangxiang Chu. Vmbench: A benchmark for perception-aligned video motion generation.arXiv preprint arXiv:2503.10076, 2025

    2025 arXiv

  16. [24]

    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. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Reco...

    2024

  17. [25]

    Towards world simulator: Crafting physical commonsense-based benchmark for video generation.arXiv preprint arXiv:2410.05363, 2024

    Fanqing Meng, Jiaqi Liao, Xinyu Tan, Wenqi Shao, Quanfeng Lu, Kaipeng Zhang, Yu Cheng, Dianqi Li, Yu Qiao, and Ping Luo. Towards world simulator: Crafting physical commonsense-based benchmark for video generation.arXiv preprint arXiv:2410.05363, 2024

    arXiv

  18. [26]

    Do generative video models learn physical principles from watching videos?arXiv preprint arXiv:2501.09038, 2025

    Saman Motamed, Laura Culp, Kevin Swersky, Priyank Jaini, and Robert Geirhos. Do generative video models learn physical principles from watching videos?arXiv preprint arXiv:2501.09038, 2025

    2025 arXiv

  19. [27]

    Dynamic time warping.Information retrieval for music and motion, pages 69–84, 2007

    Meinard Müller. Dynamic time warping.Information retrieval for music and motion, pages 69–84, 2007

    2007

  20. [28]

    Dinov2: Learning robust visual features without supervision.arXiv preprint arXiv:2304.07193, 2023

    Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy V o, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision.arXiv preprint arXiv:2304.07193, 2023

    2023 arXiv

  21. [29]

    Open-sora 2.0: Training a commercial-level video generation model in200k.arXiv preprint arXiv:2503.09642, 2025

    Xiangyu Peng, Zangwei Zheng, Chenhui Shen, Tom Young, Xinying Guo, Binluo Wang, Hang Xu, Hongxin Liu, Mingyan Jiang, Wenjun Li, et al. Open-sora 2.0: Training a commercial-level video generation model in200k.arXiv preprint arXiv:2503.09642, 2025

    2025 arXiv

  22. [30]

    Consisti2v: Enhancing visual consistency for image-to-video generation.arXiv preprint arXiv:2402.04324, 2024

    Weiming Ren, Harry Yang, Ge Zhang, Cong Wei, Xinrun Du, Stephen Huang, and Wenhu Chen. Consisti2v: Enhancing visual consistency for image-to-video generation.arXiv preprint arXiv:2402.04324, 2024

    2024 arXiv

  23. [31]

    Improved techniques for training gans

    Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, Xi Chen, and Xi Chen. Improved techniques for training gans. InAdvances in neural information processing systems, 2016

    2016

  24. [32]

    Hausdorff distances and interpolations.Computational Imaging and Vision, 12:107–114, 1998

    Jean Serra. Hausdorff distances and interpolations.Computational Imaging and Vision, 12:107–114, 1998

    1998

  25. [33]

    Denoising diffusion implicit models.arXiv preprint arXiv:2010.02502, 2020

    Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models.arXiv preprint arXiv:2010.02502, 2020

    2010 arXiv

  26. [34]

    T2v-compbench: A comprehensive benchmark for compositional text-to-video generation.arXiv preprint arXiv:2407.14505, 2024

    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.arXiv preprint arXiv:2407.14505, 2024

    2024 arXiv

  27. [35]

    FVD: A new metric for video generation

    Thomas Unterthiner, Sjoerd van Steenkiste, Karol Kurach, Raphaël Marinier, Marcin Michalski, and Sylvain Gelly. FVD: A new metric for video generation. InICLRW, 2019

    2019

  28. [36]

    The wasserstein distances.Optimal transport: old and new, pages 93–111, 2009

    Cédric Villani and Cédric Villani. The wasserstein distances.Optimal transport: old and new, pages 93–111, 2009

    2009

  29. [37]

    A framework for the greedy algorithm.Discrete Applied Mathematics, 121(1-3):247–260, 2002

    Andrew Vince. A framework for the greedy algorithm.Discrete Applied Mathematics, 121(1-3):247–260, 2002

    2002

  30. [38]

    Dynamicrafter: Animating open-domain images with video diffusion priors

    Jinbo Xing, Menghan Xia, Yong Zhang, Haoxin Chen, Wangbo Yu, Hanyuan Liu, Gongye Liu, Xintao Wang, Ying Shan, and Tien-Tsin Wong. Dynamicrafter: Animating open-domain images with video diffusion priors. InEuropean Conference on Computer Vision, pages 399–417. Springer, 2025

    2025

  31. [39]

    Learning interactive real-world simulators.arXiv preprint arXiv:2310.06114, 1(2):6, 2023

    Mengjiao Yang, Yilun Du, Kamyar Ghasemipour, Jonathan Tompson, Dale Schuurmans, and Pieter Abbeel. Learning interactive real-world simulators.arXiv preprint arXiv:2310.06114, 1(2):6, 2023. 10

    2023 arXiv

  32. [40]

    Dino: Detr with improved denoising anchor boxes for end-to-end object detection.arXiv preprint arXiv:2203.03605, 2022

    Hao Zhang, Feng Li, Shilong Liu, Lei Zhang, Hang Su, Jun Zhu, Lionel M Ni, and Heung-Yeung Shum. Dino: Detr with improved denoising anchor boxes for end-to-end object detection.arXiv preprint arXiv:2203.03605, 2022

    2022 arXiv

  33. [41]

    I2vgen-xl: High-quality image-to-video synthesis via cascaded diffusion models, 2023

    Shiwei Zhang, Jiayu Wang, Yingya Zhang, Kang Zhao, Hangjie Yuan, Zhiwu Qing, Xiang Wang, Deli Zhao, and Jingren Zhou. I2vgen-xl: High-quality image-to-video synthesis via cascaded diffusion models, 2023

    2023

  34. [42]

    Open-sora: Democratizing efficient video production for all, March 2024

    Zangwei Zheng, Xiangyu Peng, Tianji Yang, Chenhui Shen, Shenggui Li, Hongxin Liu, Yukun Zhou, Tianyi Li, and Yang You. Open-sora: Democratizing efficient video production for all, March 2024. URL https://github.com/hpcaitech/Open-Sora

    2024

  35. [43]

    Keep the first-person view of the robot unchanged. Keep the first frame of this video unchanged

    Siyuan Zhou, Yilun Du, Jiaben Chen, Yandong Li, Dit-Yan Yeung, and Chuang Gan. Robodreamer: Learning compositional world models for robot imagination.arXiv preprint arXiv:2404.12377, 2024. 11 A Appendix A.1 Additional Details on World Specification We provide implementation de...

    2024 arXiv

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.