VideoVerse: How Far is Your T2V Generator from a World Model?

## Abstract

Abstract The recent rapid advancement of Text-to-Video (T2V) generation technologies, which are critical to build “world models”, makes the existing benchmarks increasingly insufficient to evaluate state-of-the-art T2V models.
First, current evaluation dimensions, such as per-frame aesthetic quality and temporal consistency, are no longer able to differentiate state-of-the-art T2V models.
Second, event-level temporal causality, which not only distinguishes video from other modalities but also constitutes a crucial component of world models, is severely underexplored in existing benchmarks. Third, existing benchmarks lack a systematic assessment of world knowledge,
which are essential capabilities for building world models.
To address these issues, we introduce VideoVerse , a comprehensive benchmark that focuses on evaluating whether a T2V model could understand complex temporal causality and world knowledge in the real world.
We collect representative videos across diverse domains (e.g., natural landscapes, sports, indoor scenes, science fiction, chemical and physical experiments) and extract their event-level descriptions with inherent temporal causality, which are then rewritten into text-to-video prompts by independent annotators.
For each prompt, we design a suite of binary evaluation questions from the perspective of dynamic and static properties, with a total of ten carefully defined evaluation dimensions.
In total, our VideoVerse comprises 300 carefully curated prompts, involving 815 events and 793 binary evaluation questions. Consequently, a human preference aligned QA-based evaluation pipeline is developed by using modern vision-language models.
Finally, we perform a systematic evaluation of state-of-the-art open-source and closed-source T2V models on VideoVerse, providing in-depth analysis on how far the current T2V generators are from world models.

## 1 Introduction

Text-to-video (T2V) models can translate natural language into coherent and high-quality content videos, unlocking new possibilities for human–AI interaction and multimodal media creation, which
have a wide range of applications, including creative content generation (Kong et al., 2024; Wan et al., 2025; Yang et al., 2024), virtual reality (Akimoto et al., 2022), and video editing (Geyer et al., 2024; Yoon et al., 2024), etc. Along with the growing impact of T2V models, how to rigorously evaluate them has become critically important for benchmarking progress and guiding model construction, training, and deployment.
Early T2V benchmarks, such as VBench (Huang et al., 2024a) and EvalCrafter (Liu et al., 2024), primarily evaluate the generated videos at the frame level, focusing on aesthetic quality and image fidelity. Subsequent benchmarks focus on assessing semantic alignment, i.e., whether the generated video content matches the given text prompt. Recent benchmarks such as VBench2 (Zheng et al., 2025) extend the evaluation to intrinsically complex semantic alignment through VLM-based question answering, while Video-Bench (Han et al., 2025) performs the evaluation by comparing detailed video-to-text captions with the original instructions.

However, the rapid advancement of T2V technologies (Kong et al., 2024; Wan et al., 2025; Yang et al., 2024) has begun to expose the limitations of existing benchmarks. State-of-the-art T2V models have not only demonstrated strong instruction-following abilities (Ma et al., 2025; Chen et al., 2025a), but also exhibited the capacity to understand world knowledge, such as temporal and causal relations among events (Google DeepMind, 2025), while producing cinematic quality videos (Xiao et al., 2025; Gao et al., 2025).
As a consequence, existing T2V benchmarks are becoming insufficient to measure the emerging capabilities of modern T2V models and differentiate their performance for two reasons.
First, their prompts can only specify the expected content, lacking the ability to infer or predict events not explicitly described. Second, complex world knowledge and common sense reasoning remain largely unexplored in existing benchmarks.
Considering that T2V models are becoming the key elements in building “world models”, which could not only generate visual content but also simulate, reason and predict the dynamics of the physical world (Agarwal et al., 2025; Kang et al., 2024), there is a strong demand to develop a new benchmark, which
can evaluate the capabilities of T2V generators in terms of a “world model”.

Figure: Figure 1: Overview of the evaluation dimensions of VideoVerse, which are considered from two complementary perspectives: the Dynamic and the Static. Under the two categories, a total of ten dimensions, which include six world model level evaluation dimensions and four basic level evaluation dimensions.
Refer to caption: x1.png

To address the challenges mentioned above, we propose VideoVerse, a comprehensive benchmark designed to assess modern and emerging T2V models. As illustrated in Fig. [1](https://arxiv.org/html/2510.08398v2#S1.F1), VideoVerse contains a set of carefully designed evaluation dimensions from two complementary perspectives: dynamic (which should be presented across temporal frames) and static (which could be presented in a single frame). In order to effectively evaluate the capabilities of T2V models in terms of a world model, we consider two crucial aspects. The first is temporal causality at the event level, which measures whether a T2V model can produce a series of events with strong causal relationships.
A set of Event Following prompts are designed to assess the T2V model along this dimension.
The second aspect includes a series of dimensions to evaluate whether a T2V model can understand the natural world. From the static perspective, we introduce Natural Constraints and Common Sense, which play important roles in our lives. Here, Common Sense refers to socially shared conventions (e.g., “the representative animal of Sichuan Province, China is the panda” (Wikipedia, 2025a)), while Natural Constraints captures physical or chemical laws of the natural world (e.g., “concentrated sulfuric acid carbonises wood upon contact” (Wikipedia, 2025c)). In the dynamic perspective, in addition to Event Following, we consider Mechanics, Interaction, and Material Properties, which are common dynamic properties of the real world. Furthermore, as a T2V benchmark, it should also consider the basic abilities required for T2V models, including Camera Control in the dynamic perspective, and Attribution Correctness, 2D Layout, and 3D Depth in the static perspective. Finally, a total of ten dimensions (five dynamic and five static) are defined in our VideoVerse benchmark.

To better align with the world model’s ability, we design all the prompts with the guideline of “hidden semantics” (i.e., the model should predict and generate the expected scene beyond the given prompts), which requires the model to understand the physics and natural laws of the real world. For example, with the prompt shown in Fig. [1](https://arxiv.org/html/2510.08398v2#S1.F1), the model should generate content that produces bubbles when baking soda and vinegar react in the sink. In conjunction with our prompt design, we further propose a QA-based evaluation pipeline, which simulates human-like evaluation process based on powerful VLMs (Wei et al., 2025; Han et al., 2025).

Overall, with 300 carefully curated high-quality prompts, VideoVerse contains 815 events with 793 binary evaluation questions that cover different dimensions. Unlike previous benchmarks, where each prompt corresponds to a single evaluation dimension, the prompts in VideoVerse integrate multiple evaluation aspects in one prompt. This design not only provides richer and more challenging prompts for T2V models but also enables a more cost-effective evaluation procedure.

The main contributions of our work are summarized
as follows. First, we present VideoVerse, a high-quality, carefully curated benchmark with 300 prompts covering ten dimensions that span from basic instruction following to world model level reasoning. Second, we conduct an extensive evaluation of both open-source and closed-source T2V models, showing that while they perform comparably on traditional benchmarks, their performance differs largely on VideoVerse, especially in dimensions requiring causal reasoning and world knowledge. Third, our analysis demonstrates that current T2V models still fall short of world models, indicating new challenges and directions for future research in this rapidly evolving field.

## 2 Related Work

Text-to-Video (T2V) Models. Earlier T2V models (Ho et al., 2022; Hong et al., 2023; Chen et al., 2023; Wang et al., 2024) are limited to short clips with limited expressiveness, while recent models have demonstrated substantial improvements by leveraging larger backbones and higher quality training data (Kong et al., 2024; Wan et al., 2025; Ma et al., 2025; Chen et al., 2025a; Esser et al., 2023; Zheng et al., 2024; Peng et al., 2025). HunyuanVideo (Kong et al., 2024) and Wan series (Wan et al., 2025) employ DiT-based architectures and considerably enhance the performance of open-source models, while StepVideo (Ma et al., 2025), with its 30B parameters, achieves state-of-the-art results across multiple dimensions. Meanwhile, closed-source models generally outperform their open-source counterparts (Google DeepMind, 2025; Brooks et al., 2024; Pika Lab, 2025; Kling, 2025; MiniMax, 2025), particularly in video length, visual fidelity, and adherence to textual instructions. The rapid progress of T2V models highlights their potentials to become world models, which, however, poses challenges on how to accurately evaluate their performance along such perspectives.

T2V Model Evaluation. Earlier T2V benchmarks (Huang et al., 2024a; Liu et al., 2024; Huang et al., 2024b) rely primarily on frame-level aesthetic and image quality metrics such as FID (Seitzer, 2020), FVD (Unterthiner et al., 2019), IS (Salimans et al., 2016), and basic video attributes such as subject consistency. With the rapid development of T2V models, frame quality has reached human-perceptual standards. Benchmarks shifted their focus to assessing whether the generated video content matches the given text prompt. For example, VBench2 (Zheng et al., 2025) employs VLM-based question answering and expert models to evaluate complex semantic alignment, while Video-Bench (Han et al., 2025) shifts its evaluation entirely into the textual space by aligning video-to-text captions with instructions.
StoryEval (Wang et al., 2025a), an event-centric T2V benchmark, evaluates whether the events described in the prompt will occur, but it neglects the temporal causality among events and the static attributes of videos. Howerver, the prompts in these benchmarks lack a “hidden
semantics” guideline (i.e., the model should generate expected behaviours beyond what is explicitly stated) and fail to systematically incorporate world knowledge (Niu et al., 2025).

Evaluation of T2V Models as “World Model”. T2V models have shown the potential to be “world models” (Huang et al., 2025; Chen et al., 2025b), yet they still struggle to generate realistic content aligned with the real world (Wang et al., 2025b; Kang et al., 2024). Some works (Bansal et al., 2024; 2025; Li et al., 2024; Meng et al., 2024; Qin et al., 2025) focus on evaluating T2V models’ ability to capture physical laws or simulate the real world. However, these studies consider only physical regularities, particularly motion laws, overlooking broader aspects of world knowledge, such as state changes, chemical interaction, cultural common sense, etc.
In addition, previous T2V Benchmarks (Zheng et al., 2025; Huang et al., 2024a; Liu et al., 2024) only consider the content explicitly described in the prompt, but lack the ability to evaluate the T2V models in terms of hidden semantics, which are primitive elements of a “World Model”.

To this end, we introduce VideoVerse, a comprehensive benchmark designed for evaluating modern T2V models’ ability from basic instruction following to world model level understanding with a simple, easy-to-use, and human-preference-aligned evaluation protocol. In addition, our compact prompt design enables efficient assessment with a limited number of well-designed prompts, a property that is particularly useful for benchmarking increasingly large T2V models.

## 3 Construction of VideoVerse Bench

Unlike existing T2V benchmarks, which construct prompts in a relatively straightforward manner to cover a wide range of visual elements, we argue that a world model level T2V models should not only generate videos that align with the text prompt (e.g., whether the objects appear or whether the attributes such as color and texture are correct), but also demonstrate strong capabilities in understanding the implicit temporal and logical relations among events, as well as the world knowledge such as Natural Constraints and Common Sense. To this end, we define ten evaluation dimensions to assess the quality of generated videos from both static and dynamic perspectives, as illustrated in Fig. [1](https://arxiv.org/html/2510.08398v2#S1.F1) and detailed in the following sections.

### 3.1 Static Dimensions

Static dimensions refer to those facts or properties that can be evaluated from a single frame. The following five static dimensions are defined in VideoVerse:

Natural Constraints, which evaluates whether the generated content adheres to natural scientific laws. For example, a lake at $-20^{\circ}\mathrm{C}$ should be frozen.

Common Sense, which evaluates whether the generated content aligns with the cultural or contextual common sense knowledge implied in the prompt. For example, a “tree representative of Japanese culture” should be a cherry blossom tree.

Attribution Correctness, which evaluates whether the objects mentioned in the prompt appear in the generated video, and whether their specified attributes, such as colour, material and shape, are correctly generated.

2D Layout, which evaluates whether the 2D spatial arrangement among the objects mentioned in the prompt is correctly represented.

3D Depth, which evaluates whether the perspective relationships among the objects mentioned in the prompt, such as which objects are in the foreground or background, are correctly represented.

### 3.2 Dynamic Dimensions

Dynamic dimensions refer to those facts or properties that can only be evaluated by understanding the temporal dynamics in a video (which cannot be represented in a single frame). The following five dynamic dimensions are defined in VideoVerse:

Event Following, which evaluates whether the T2V model generate a temporally causal sequence of events specified in the prompt.

Mechanics, which evaluate whether an object in the video follows the laws of mechanics in its motion. This does not require interaction with other objects. For example, when a dumbbell is released from the hand, it should fall to the ground due to gravity, without a change in shape.

Interaction, which evaluates whether the interactions between objects are physically reasonable. Here, our focus is on interactions that involve direct object contact, regardless of material properties. For example, shaving with a razor should result in shorter facial hair.

Material Properties, which evaluate whether an object’s behaviour is consistent with its intrinsic material properties, even without explicit contact with other objects. For example, chocolate should gradually melt when heated.

Camera Control, which evaluates whether the camera operations specified in the prompt, such as focus control and motion trajectory, are executed correctly.

### 3.3 Prompt Construction

The prompts in our VideoVerse are drawn from three distinct domains with different objectives: (1) Daily Life, (2) Scientific Experiment, and (3) Science Fiction. Each domain undergoes a tailored preprocessing pipeline, as detailed below.

Daily Life: We sample a large set of videos from the real-world dataset ActivityNet Caption (Krishna et al., 2017). Although ActivityNet Caption provides captions with multiple events, which is consistent with the event-centric design philosophy of our VideoVerse, it suffers from two limitations: (i) not all events exhibit temporal causality, which is a crucial requirement in our benchmark, and (ii) its captions often correspond to overly long video segments, making some of them unsuitable for constructing concise T2V prompts.
To address these issues, we use GPT-4o to filter and refine the original captions, yielding suitable prompts for this domain.

Scientific Experiment: Although other categories of prompts occasionally include prompts related to natural science, they are not explicitly designed for this purpose. To better evaluate the world model capabilities of T2V models, we manually collect a set of prompts derived from high-school-level natural science experiments from the web and incorporate them into this domain.

Science Fiction: Unlike the first two domains, which focus on realistic scenarios, this category focuses on imaginative, non-realistic content. It is designed to test T2V models’ out-of-domain generalization, as training corpora rarely contain fictional scenarios. We curate science-fiction prompts from VidProM (Wang & Yang, 2024), a community-collected dataset, and apply GPT-4o to clean irrelevant tokens. The resulting set forms the source prompts for this domain.

After collecting source prompts from the three domains, we employ a unified pipeline that takes advantage of GPT-4o as an event-causality extractor, which is illustrated in Fig. [4](https://arxiv.org/html/2510.08398v2#A1.F4) in Appendix [A](https://arxiv.org/html/2510.08398v2#A1). Specifically, GPT-4o identifies causal relationships between events within a video and organizes them into an initial raw prompt.
However, these raw prompts only capture event-level structures, but do not include the full range of dimensions required for evaluation.
To mitigate these issues, we invite independent human annotators to enrich the raw prompts with appropriate semantic content for the relevant evaluation dimensions, refining them into the final T2V prompts.
This manual process ensures prompt quality and fairness of the evaluation. Specifically, for each raw prompt (comprising events extracted by GPT-4o), independent annotators select the most appropriate evaluation dimensions and revise the raw prompt accordingly.
Each added dimension is paired with a corresponding binary evaluation question. Consider that VideoVerse requires annotations to capture hidden semantics and world-model-level knowledge, all annotators hold at least a bachelor’s degree.
Furthermore, to balance different evaluation dimensions, annotators periodically review their prior annotations and adjust subsequent labeling preferences to reduce bias.
Beyond ensuring fairness, manual refinement also provides interpretability and reliability that cannot be guaranteed by fully automated methods, strengthening the credibility of our VideoVerse.

### 3.4 Evaluation Protocol

Traditional benchmarks primarily rely on various expert models to evaluate frame-level image quality. Subsequent benchmarks employ large vision-language models (VLMs) for QA-based evaluation; however, these VLMs have limited video understanding capabilities. Moreover, in these benchmarks, the text instruction of the generated video is directly exposed to the VLM, which often leads to hallucination issues, i.e., the VLM assumes that certain elements mentioned in the instruction will appear in the video even if they do not.

Unlike previous benchmarks, VideoVerse not only assesses the fundamental capabilities of T2V models but also measures their gap to a comprehensive world model. To enable such a complex and holistic evaluation, we leverage state-of-the-art VLMs with rich world knowledge and strong reasoning abilities. Different from previous works, which expose the full text instruction to the VLM, we provide dimension-specific binary questions to the VLM, thereby mitigating the hallucination issues. In particular, our VLM-based evaluation protocol consists of two components.

Temporal Causality Evaluation. StoryEval (Wang et al., 2025a) also evaluates T2V models based on event-level reasoning. However, it only verifies whether the described events are generated, without considering the temporal and causal correlations among them.

We adopt the Longest Common Subsequence (LCS) algorithm (Wikipedia, 2025b) as the evaluation protocol for Event Following performance.
Specifically, we first use a powerful VLM to identify whether each event occurs in the generated video and extract the corresponding sequence of events.
Let the ground truth event sequence be $E=\{e_{1},e_{2},\dots,e_{n}\}$ and the predicted event sequence be $\hat{E}=\{\hat{e}_{1},\hat{e}_{2},\dots,\hat{e}_{\hat{m}}\}$,
where events absent from the generated video are not output by the VLM.
We then compute the longest subsequence of $\hat{E}$ that aligns with $E$, and take its length as the Event Following score of the generated video.

Prompt-specific Dimension Evaluation. As shown in Fig. [1](https://arxiv.org/html/2510.08398v2#S1.F1), VideoVerse defines a total of ten evaluation dimensions.
Apart from the Event Following dimension, which is included for every prompt, the other dimensions vary across prompts, and the number of binary questions associated with each dimension is also varying.
For the $m$ binary evaluation questions beyond Event Following, we conduct $m$ independent interactions with the VLM, and the number of correctly answered questions is taken as the score for these additional dimensions.

Given the characteristics of our evaluation protocol and following previous works (Fu et al., 2024), we adopt a cumulative scoring strategy rather than a percentage score as the final evaluation metric for a T2V model in VideoVerse.
Suppose that a prompt $P$ contains $N$ evaluation dimensions (excluding Event Following),
where the $i$-th dimension is associated with $k_{i}$ binary evaluation questions.
Let model $M$ generate a video $V$ under prompt $P$.
The score of $V$ is defined as:

$$ $S(V)=\text{LCS}(V)\;+\;\sum\nolimits_{i=1}^{N}\sum\nolimits_{j=1}^{k_{i}}\mathbb{I}\!\big(\text{Eval}(V,q_{i,j})=\text{Yes}\big),$ (1) $$

where $\text{LCS}(V)$ denotes the LCS score for Event Following,
$q_{i,j}$ is the $j$-th binary evaluation question under the $i$-th dimension,
and $\mathbb{I}(\cdot)$ is the indicator function that equals $1$ if the condition holds and 0 otherwise.
Thus, the final score of model $M$ on prompt $P$ is given by $S(V)$. We provide the prompts used in the evaluation in the Appendix [B](https://arxiv.org/html/2510.08398v2#A2).

### 3.5 Comparison with other T2V Benchmarks

Early T2V benchmarks primarily evaluate video quality using domain-specific expert models with frame-level aesthetic and image quality metrics. Later benchmarks shift their focus toward assessing whether the generated video content matches the given text prompt. However, these benchmarks lack the ability to evaluate T2V generators from the perspective of a world model.
Compared with previous benchmarks, VideoVerse substantially increases the complexity of prompts by introducing highly diverse scenes, characters, and event content.
Each prompt contains events with implicit causal and temporal relations, while incorporating rich world knowledge.

To quantify the distinctions introduced by the VideoVerse prompt set, we use CLIP to extract semantic embeddings of prompts from mainstream benchmarks and compute their cosine similarity, as shown in Fig. [2](https://arxiv.org/html/2510.08398v2#S3.F2).
The results reveal that existing benchmarks contain a considerable degree of semantic redundancy, whereas VideoVerse achieves the highest semantic uniqueness.
Moreover, the prompts in existing benchmarks are overly simplistic in terms of length and cannot represent the complex instructions that users typically provide to world model level T2V systems.
In contrast, thanks to its careful and diverse design, VideoVerse not only exhibits a significantly longer average prompt length than existing benchmarks but also demonstrates a more natural length distribution.

Figure: Figure 2: Left: We extract CLIP embeddings of prompts from mainstream T2V benchmarks and compute their cosine similarity. We see that existing benchmarks contain a large number of redundant prompts. Middle: Users typically provide complex instructions when interacting with world model level T2V systems, yet existing benchmarks generally consist of overly short prompts. Right: The prompt length distribution of VideoVerse aligns closely with natural usage patterns. We also provide more comparisons with other T2V benchmarks in the Appendix [D](https://arxiv.org/html/2510.08398v2#A4).
Refer to caption: x2.png

## 4 Experiments

In this section, we evaluate the major T2V models on VideoVerse and provide detailed analyses of their performance. We also conduct a user study to examine whether our evaluation protocol is sufficient and aligned with human perception. The T2V models we evaluated include CogVideoX1.5-5B (Yang et al., 2024), SkyReels-V2-14B Chen et al. (2025a), HunyuanVideo (Kong et al., 2024), OpenSora2.0 (Peng et al., 2025), Wan2.1-14B (Wan et al., 2025), Wan2.2-A14B (Wan et al., 2025), Hailuo (MiniMax, 2025) ,Veo3 (Google DeepMind, 2025) and Sora-2 (OpenAI, 2025). The deployment details of those models can be found in Appendix [C](https://arxiv.org/html/2510.08398v2#A3).

### 4.1 Main Results

Tab. [1](https://arxiv.org/html/2510.08398v2#S4.T1) presents the evaluation results of the T2V models on VideoVerse using Gemini 2.5 Pro (Comanici et al., 2025). Among open-source models, while Wan2.2-A14B achieves the highest overall score, the best performers across world model level dimensions diverge. OpenSora2.0 demonstrates strong results in Common Sense and Natural Constraints in the static category. This can be attributed to its design: unlike other T2V models, OpenSora2.0 conditions video generation on the outputs from the powerful T2I model Flux (Labs et al., 2025), which significantly enhances its generation capability along static world model level dimensions. In contrast, SkyReels-V2 (L) achieves the best performance in Interaction, which is because, compared with other dynamic dimensions, Interaction emphasises the interactions between objects; longer generation length provides it with a broader context to model such behaviours. For the remaining world model level dimensions, Wan2.2-A14B outperforms the other open-source models for its advanced architecture and large-scale training data. Across the basic level dimensions, most open-source models perform comparably except for the Camera Control dimension, which requires strong instruction-following capability.

However, open-source models still lag considerably behind closed-source systems in all evaluation dimensions. Veo-3 achieves the best overall performance, establishing state-of-the-art results in most dimensions. Similar to open-source systems, closed-source models show comparable performance on the basic level dimensions. However, their abilities diverge markedly at the world model level, reflecting the fact that even for the most advanced closed-source models, reasoning about the world remains a substantial challenge. We will discuss this further in Sec. [4.2](https://arxiv.org/html/2510.08398v2#S4.SS2).

Table: Table 1: Performance of Open-Source and Closed-Source models on VideoVerse based on Gemini2.5 pro, with the best values highlighted in bold. The light green columns represent the world model level dimensions, while the other columns represent the basic level dimensions. We employ the 5B variant of CogVideoX1.5 and the 14B variant of SkyReels-V2. (S) and (L) denote the “Short Video” (5s) and “Long Video” (10s) settings, respectively. *Due to Sora’s security review, some videos are not successfully generated. Despite this, Sora-2 still achieved the SOTA performance.

### 4.2 Discussions

Will Video Length Influence Event Following Performance?
All prompts in our VideoVerse contain at least one event that requires T2V models to generate content along the temporal dimension. Although modern T2V models can generate longer videos than earlier ones, the length is still limited to a few seconds. This raises a question: Is their limited performance on Event Following primarily due to the short video length, which restricts the number of events that can be generated?

Based on our experimental results, the answer is No. As shown in Tab. [1](https://arxiv.org/html/2510.08398v2#S4.T1), Veo-3 produces only $8$-second videos, yet it consistently outperforms models with $10$-second outputs across all dimensions. Moreover, for models capable of generating $10$-second videos, their performance on Event Following shows no clear advantage over shorter ones (e.g., CogVideoX1.5-S/L, SkyReels-V2-S/L). Notably, the open-source Wan2.2-A14B, limited to 5-second outputs, still exceeds other open-source models in Event Following, including those generating 10-second videos.
Therefore, for the prompts in VideoVerse, the current length of generated videos already provides sufficient temporal capacity for T2V models to process the required events.

How Far are the Current T2V Models from a “World Model”?
We show the performance of typical open-source and closed-source T2V models in terms of basic abilities and world model level abilities in Tab. [2](https://arxiv.org/html/2510.08398v2#S4.T2). We see that they perform comparably in basic ability, but their gap in world model level abilities is much larger. The powerful Veo-3 model demonstrates a high performance in world knowledge. It achieves 228 points (w/o EF) out of the total of 315 world-model scores (72.4%, 228/315), while it achieves 384 points out of the total of 478 scores (80.3%, 384/478) in basic dimensions. The statistics for each category of evaluation dimensions are provided in Appendix [D](https://arxiv.org/html/2510.08398v2#A4)). This observation indicates that, despite the impressive generative capabilities of current T2V models, they are still far from achieving the level of a true “World Model.”

There are two main limitations of current T2V models. i) “Hidden” Semantics Following. T2V models often restrict their generation to the surface-level semantics explicitly mentioned in the prompt, while ignoring implicit or hidden semantics beyond the text.
ii) Limited Understanding of the real world. Although current models can sometimes generate contents explicitly presented in prompts, they often fail to generate reasonable output when additional semantic constraints grounded in real-world knowledge are introduced. Please refer to Appendix [E](https://arxiv.org/html/2510.08398v2#A5) for some cases.

Figure: Figure 3: Case study of T2V models’ performance on our VideoVerse. Gemini 2.5 Pro is used as the evaluator. Wan 2.1 and Hunyuan successfully generate the corresponding attribution content (horse’s coat glistens) but struggle with Event Following and Common Sense, whereas Veo-3 demonstrates strong performance across all dimensions.
Refer to caption: x3.png

Table: Table 2: Performance gap between open-source and closed-source T2V models. Open-source models exhibit comparable performance to closed-source models on basic dimensions, whereas the gap is more pronounced on world-model dimensions. Since the Event Following (EF) dimension uses LCS as its metric score, we also present the statistics without EF (w/o EF). Notably, even the advanced closed-source model Veo-3 has much room to improve to be a world model.

### 4.3 Case Study and User Study

Table: Table 3: User study results: Gemini 2.5 Pro demonstrates high consistency with human judgment in evaluating T2V models on VideoVerse.

Fig. [3](https://arxiv.org/html/2510.08398v2#S4.F3) presents a case from VideoVerse. We see that the closed-source model Veo-3 not only successfully generates all events but also correctly understands common sense knowledge: “the steed of Tang Sanzang……” refers to a white horse. In contrast, open-source models such as Wan 2.1 and Hunyuan generate content accurately in the Attribution Correctness dimension but struggle with world model level dimensions, such as Event Following and Common Sense. This highlights the significant gap between open-source and closed-source models at the world model level dimensions.

We employ the SOTA video understanding VLM, Gemini 2.5 Pro, to evaluate the T2V models on VideoVerse. To examine how well Gemini 2.5 Pro aligns with human judgment, we conduct a user study using 15 videos generated from 11 prompts in our VideoVerse, spanning over the ten evaluation dimensions. A total of 11 volunteers are invited to participate in the study, and there are 594 questions in the evaluation. Following the same protocol as in Sec. [3.4](https://arxiv.org/html/2510.08398v2#S3.SS4), participants are provided with the video and the corresponding question, mirroring the VLM evaluation setting. As shown in Tab. [3](https://arxiv.org/html/2510.08398v2#S4.T3), there is a high consistency ($>$90%) between human judgment and VLM evaluation in different dimensions. More details are provided in Appendix [F](https://arxiv.org/html/2510.08398v2#A6).
Although Gemini2.5 Pro demonstrates high alignment with human judgment, it is a closed-source commercial model that has limited accessibility to the community. To facilitate broader adoption of VideoVerse, we also provide results using other open-source VLMs as evaluators, as detailed in Appendix [G](https://arxiv.org/html/2510.08398v2#A7).

## 5 Conclusion

In this paper, we presented VideoVerse, the first benchmark designed to evaluate T2V models from a world model perspective, which has 300 carefully constructed prompts from 10 well-designed evaluation dimensions. Through systematic evaluation, we find that although substantial progress has been made by current T2V models, they still have gaps in terms of world models. Meanwhile, the performance of open-source T2V models is much lower than that of their closed-source counterparts. Our findings highlighted the challenges of T2V research and underscored the limitations of current T2V models in understanding, reasoning, and generalising in dynamic environments. We hope that VideoVerse can facilitate the development of next-generation T2V models.

Limitations. While VideoVerse is the first attempt to evaluate T2V models through the lens of a world model, it has several limitations. First, the definition of a “World Model” is inherently multi-faceted, and our benchmark captures only a subset of key dimensions. Moreover, VideoVerse currently focuses on 2D T2V models, whereas emerging research on 3D and open-world generation highlights other critical aspects of world modeling.

## References

- Agarwal et al. (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.
- Akimoto et al. (2022)
Naofumi Akimoto, Yuhi Matsuo, and Yoshimitsu Aoki.
Diverse plausible 360-degree image outpainting for efficient 3dcg background creation.
In *Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)*, 2022.
- An et al. (2025a)
Ruichuan An, Sihan Yang, Ming Lu, Renrui Zhang, Kai Zeng, Yulin Luo, Jiajun Cao, Hao Liang, Ying Chen, Qi She, Shanghang Zhang, and Wentao Zhang.
Mc-llava: Multi-concept personalized vision-language model, 2025a.
URL [https://arxiv.org/abs/2411.11706](https://arxiv.org/abs/2411.11706).
- An et al. (2025b)
Ruichuan An, Sihan Yang, Renrui Zhang, Zijun Shen, Ming Lu, Gaole Dai, Hao Liang, Ziyu Guo, Shilin Yan, Yulin Luo, Bocheng Zou, Chaoqun Yang, and Wentao Zhang.
Unictokens: Boosting personalized understanding and generation via unified concept tokens, 2025b.
URL [https://arxiv.org/abs/2505.14671](https://arxiv.org/abs/2505.14671).
- Bansal et al. (2024)
Hritik Bansal, Zongyu Lin, Tianyi Xie, Zeshun Zong, Michal Yarom, Yonatan Bitton, Chenfanfu Jiang, Yizhou Sun, Kai-Wei Chang, and Aditya Grover.
Videophy: Evaluating physical commonsense for video generation.
*arXiv preprint arXiv:2406.03520*, 2024.
- Bansal et al. (2025)
Hritik Bansal, Clark Peng, Yonatan Bitton, Roman Goldenberg, Aditya Grover, and Kai-Wei Chang.
Videophy-2: A challenging action-centric physical commonsense evaluation in video generation.
*arXiv preprint arXiv:2503.06800*, 2025.
- Brooks et al. (2024)
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.
*OpenAI Blog*, 1(8):1, 2024.
- Chen et al. (2025a)
Guibin Chen, Dixuan Lin, Jiangping Yang, Chunze Lin, Junchen Zhu, Mingyuan Fan, Hao Zhang, Sheng Chen, Zheng Chen, Chengcheng Ma, Weiming Xiong, Wei Wang, Nuo Pang, Kang Kang, Zhiheng Xu, Yuzhe Jin, Yupeng Liang, Yubing Song, Peng Zhao, Boyuan Xu, Di Qiu, Debang Li, Zhengcong Fei, Yang Li, and Yahui Zhou.
Skyreels-v2: Infinite-length film generative model, 2025a.
URL [https://arxiv.org/abs/2504.13074](https://arxiv.org/abs/2504.13074).
- Chen et al. (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.
- Chen et al. (2025b)
Yubin Chen, Xuyang Guo, Zhenmei Shi, Zhao Song, and Jiahao Zhang.
T2vworldbench: A benchmark for evaluating world knowledge in text-to-video generation, 2025b.
URL [https://arxiv.org/abs/2507.18107](https://arxiv.org/abs/2507.18107).
- Comanici et al. (2025)
Gheorghe Comanici, Eric Bieber, Mike Schaekermann, Ice Pasupat, Noveen Sachdeva, Inderjit Dhillon, Marcel Blistein, Ori Ram, Dan Zhang, Evan Rosen, and Luke Marris et al.
Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities, 2025.
URL [https://arxiv.org/abs/2507.06261](https://arxiv.org/abs/2507.06261).
- Esser et al. (2023)
Patrick Esser, Johnathan Chiu, Parmida Atighehchian, Jonathan Granskog, and Anastasis Germanidis.
Structure and content-guided video synthesis with diffusion models.
In *Proceedings of the IEEE/CVF international conference on computer vision*, pp. 7346–7356, 2023.
- Fu et al. (2024)
Chaoyou Fu, Peixian Chen, Yunhang Shen, Yulei Qin, Mengdan Zhang, Xu Lin, Jinrui Yang, Xiawu Zheng, Ke Li, Xing Sun, Yunsheng Wu, and Rongrong Ji.
Mme: A comprehensive evaluation benchmark for multimodal large language models, 2024.
URL [https://arxiv.org/abs/2306.13394](https://arxiv.org/abs/2306.13394).
- Gao et al. (2025)
Yu Gao, Haoyuan Guo, Tuyen Hoang, Weilin Huang, Lu Jiang, Fangyuan Kong, Huixia Li, Jiashi Li, Liang Li, Xiaojie Li, et al.
Seedance 1.0: Exploring the boundaries of video generation models.
*arXiv preprint arXiv:2506.09113*, 2025.
- Geyer et al. (2024)
Michal Geyer, Omer Bar-Tal, Shai Bagon, and Tali Dekel.
Tokenflow: Consistent diffusion features for consistent video editing.
In *The Twelfth International Conference on Learning Representations*, 2024.
URL [https://openreview.net/forum?id=lKK50q2MtV](https://openreview.net/forum?id=lKK50q2MtV).
- Google DeepMind (2025)
Google DeepMind.
Veo - google deepmind, 2025.
[https://deepmind.google/models/veo/](https://deepmind.google/models/veo/) [2025.09.08].
- Han et al. (2025)
Hui Han, Siyuan Li, Jiaqi Chen, Yiwen Yuan, Yuling Wu, Yufan Deng, Chak Tou Leong, Hanwen Du, Junchen Fu, Youhua Li, et al.
Video-bench: Human-aligned video generation benchmark.
In *Proceedings of the Computer Vision and Pattern Recognition Conference*, pp. 18858–18868, 2025.
- Ho et al. (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.
- Hong et al. (2023)
Wenyi Hong, Ming Ding, Wendi Zheng, Xinghan Liu, and Jie Tang.
Cogvideo: Large-scale pretraining for text-to-video generation via transformers.
In *The Eleventh International Conference on Learning Representations*, 2023.
URL [https://openreview.net/forum?id=rB6TpjAuSRy](https://openreview.net/forum?id=rB6TpjAuSRy).
- Huang et al. (2025)
Siqiao Huang, Jialong Wu, Qixing Zhou, Shangchen Miao, and Mingsheng Long.
Vid2world: Crafting video diffusion models to interactive world models, 2025.
URL [https://arxiv.org/abs/2505.14357](https://arxiv.org/abs/2505.14357).
- Huang et al. (2024a)
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.
In *Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition*, pp. 21807–21818, 2024a.
- Huang et al. (2024b)
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*, 2024b.
- Kang et al. (2024)
Bingyi Kang, Yang Yue, Rui Lu, Zhijie Lin, Yang Zhao, Kaixin Wang, Huang Gao, and Jiashi Feng.
How far is video generation from world model? – a physical law perspective.
*arXiv preprint arXiv:2406.16860*, 2024.
- Kling (2025)
Kling.
Klingai: Image to video.
[https://app.klingai.com/global](https://app.klingai.com/global), 2025.
Accessed: 2025-09-08.
- Kong et al. (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.
- Krishna et al. (2017)
Ranjay Krishna, Kenji Hata, Frederic Ren, Li Fei-Fei, and Juan Carlos Niebles.
Dense-captioning events in videos.
In *Proceedings of the IEEE international conference on computer vision*, pp. 706–715, 2017.
- Labs et al. (2025)
Black Forest Labs, Stephen Batifol, Andreas Blattmann, Frederic Boesel, Saksham Consul, Cyril Diagne, Tim Dockhorn, Jack English, Zion English, Patrick Esser, Sumith Kulal, Kyle Lacey, Yam Levi, Cheng Li, Dominik Lorenz, Jonas Müller, Dustin Podell, Robin Rombach, Harry Saini, Axel Sauer, and Luke Smith.
Flux.1 kontext: Flow matching for in-context image generation and editing in latent space, 2025.
URL [https://arxiv.org/abs/2506.15742](https://arxiv.org/abs/2506.15742).
- Li et al. (2024)
Xuanyi Li, Daquan Zhou, Chenxu Zhang, Shaodong Wei, Qibin Hou, and Ming-Ming Cheng.
Sora generates videos with stunning geometrical consistency.
*arXiv preprint arXiv:2402.17403*, 2024.
- Lin et al. (2023)
Weifeng Lin, Ziheng Wu, Jiayu Chen, Jun Huang, and Lianwen Jin.
Scale-aware modulation meet transformer.
In *Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)*, pp. 6015–6026, October 2023.
- Lin et al. (2025a)
Weifeng Lin, Xinyu Wei, Ruichuan An, Peng Gao, Bocheng Zou, Yulin Luo, Siyuan Huang, Shanghang Zhang, and Hongsheng Li.
Draw-and-understand: Leveraging visual prompts to enable mllms to comprehend what you want, 2025a.
URL [https://arxiv.org/abs/2403.20271](https://arxiv.org/abs/2403.20271).
- Lin et al. (2025b)
Weifeng Lin, Xinyu Wei, Ruichuan An, Tianhe Ren, Tingwei Chen, Renrui Zhang, Ziyu Guo, Wentao Zhang, Lei Zhang, and Hongsheng Li.
Perceive anything: Recognize, explain, caption, and segment anything in images and videos, 2025b.
URL [https://arxiv.org/abs/2506.05302](https://arxiv.org/abs/2506.05302).
- Lin et al. (2025c)
Weifeng Lin, Xinyu Wei, Renrui Zhang, Le Zhuo, Shitian Zhao, Siyuan Huang, Huan Teng, Junlin Xie, Yu Qiao, Peng Gao, and Hongsheng Li.
Pixwizard: Versatile image-to-image visual assistant with open-language instructions, 2025c.
URL [https://arxiv.org/abs/2409.15278](https://arxiv.org/abs/2409.15278).
- Liu et al. (2024)
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 *Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition*, pp. 22139–22149, 2024.
- Luo et al. (2024)
Yulin Luo, Ruichuan An, Bocheng Zou, Yiming Tang, Jiaming Liu, and Shanghang Zhang.
Llm as dataset analyst: Subpopulation structure discovery with large language model, 2024.
URL [https://arxiv.org/abs/2405.02363](https://arxiv.org/abs/2405.02363).
- Ma et al. (2025)
Guoqing Ma, Haoyang Huang, Kun Yan, Liangyu Chen, Nan Duan, Shengming Yin, Changyi Wan, Ranchen Ming, Xiaoniu Song, Xing Chen, Yu Zhou, Deshan Sun, Deyu Zhou, Jian Zhou, Kaijun Tan, Kang An, Mei Chen, Wei Ji, Qiling Wu, Wen Sun, Xin Han, Yanan Wei, Zheng Ge, Aojie Li, Bin Wang, Bizhu Huang, Bo Wang, Brian Li, Changxing Miao, Chen Xu, Chenfei Wu, Chenguang Yu, Dapeng Shi, Dingyuan Hu, Enle Liu, Gang Yu, Ge Yang, Guanzhe Huang, Gulin Yan, Haiyang Feng, Hao Nie, Haonan Jia, Hanpeng Hu, Hanqi Chen, Haolong Yan, Heng Wang, Hongcheng Guo, Huilin Xiong, Huixin Xiong, Jiahao Gong, Jianchang Wu, Jiaoren Wu, Jie Wu, Jie Yang, Jiashuai Liu, Jiashuo Li, Jingyang Zhang, Junjing Guo, Junzhe Lin, Kaixiang Li, Lei Liu, Lei Xia, Liang Zhao, Liguo Tan, Liwen Huang, Liying Shi, Ming Li, Mingliang Li, Muhua Cheng, Na Wang, Qiaohui Chen, Qinglin He, Qiuyan Liang, Quan Sun, Ran Sun, Rui Wang, Shaoliang Pang, Shiliang Yang, Sitong Liu, Siqi Liu, Shuli Gao, Tiancheng Cao, Tianyu Wang, Weipeng Ming, Wenqing He, Xu Zhao, Xuelin Zhang,
Xianfang Zeng, Xiaojia Liu, Xuan Yang, Yaqi Dai, Yanbo Yu, Yang Li, Yineng Deng, Yingming Wang, Yilei Wang, Yuanwei Lu, Yu Chen, Yu Luo, Yuchu Luo, Yuhe Yin, Yuheng Feng, Yuxiang Yang, Zecheng Tang, Zekai Zhang, Zidong Yang, Binxing Jiao, Jiansheng Chen, Jing Li, Shuchang Zhou, Xiangyu Zhang, Xinhao Zhang, Yibo Zhu, Heung-Yeung Shum, and Daxin Jiang.
Step-video-t2v technical report: The practice, challenges, and future of video foundation model, 2025.
URL [https://arxiv.org/abs/2502.10248](https://arxiv.org/abs/2502.10248).
- Meng et al. (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.
- MiniMax (2025)
MiniMax.
Hailuo ai: Transform idea to visual with ai, 2025.
[https://hailuoai.video/](https://hailuoai.video/) [2025.09.08].
- Niu et al. (2025)
Yuwei Niu, Munan Ning, Mengren Zheng, Weiyang Jin, Bin Lin, Peng Jin, Jiaqi Liao, Chaoran Feng, Kunpeng Ning, Bin Zhu, and Li Yuan.
Wise: A world knowledge-informed semantic evaluation for text-to-image generation, 2025.
URL [https://arxiv.org/abs/2503.07265](https://arxiv.org/abs/2503.07265).
- OpenAI (2025)
OpenAI.
Sora-2, 2025.
https://openai.com/index/sora-2/.
- Peng et al. (2025)
Xiangyu Peng, Zangwei Zheng, Chenhui Shen, Tom Young, Xinying Guo, Binluo Wang, Hang Xu, Hongxin Liu, Mingyan Jiang, Wenjun Li, Yuhui Wang, Anbang Ye, Gang Ren, Qianran Ma, Wanying Liang, Xiang Lian, Xiwen Wu, Yuting Zhong, Zhuangyan Li, Chaoyu Gong, Guojun Lei, Leijun Cheng, Limin Zhang, Minghao Li, Ruijie Zhang, Silan Hu, Shijie Huang, Xiaokang Wang, Yuanheng Zhao, Yuqi Wang, Ziang Wei, and Yang You.
Open-sora 2.0: Training a commercial-level video generation model in 200k.
*arXiv preprint arXiv:2503.09642*, 2025.
- Pika Lab (2025)
Pika Lab.
Pika, 2025.
https://pika.art/ [2025.09.09].
- Qin et al. (2025)
Yiran Qin, Zhelun Shi, Jiwen Yu, Xijun Wang, Enshen Zhou, Lijun Li, Zhenfei Yin, Xihui Liu, Lu Sheng, Jing Shao, LEI BAI, and Ruimao Zhang.
Worldsimbench: Towards video generation models as world simulators.
In *Forty-second International Conference on Machine Learning*, 2025.
URL [https://openreview.net/forum?id=j9pVnmulQm](https://openreview.net/forum?id=j9pVnmulQm).
- Salimans et al. (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, 2016.
- Seitzer (2020)
Maximilian Seitzer.
pytorch-fid: FID Score for PyTorch.
[https://github.com/mseitzer/pytorch-fid](https://github.com/mseitzer/pytorch-fid), August 2020.
Version 0.3.0.
- Tong et al. (2025)
Chengzhuo Tong, Ziyu Guo, Renrui Zhang, Wenyu Shan, Xinyu Wei, Zhenghao Xing, Hongsheng Li, and Pheng-Ann Heng.
Delving into rl for image generation with cot: A study on dpo vs. grpo, 2025.
URL [https://arxiv.org/abs/2505.17017](https://arxiv.org/abs/2505.17017).
- Unterthiner et al. (2019)
Thomas Unterthiner, Sjoerd van Steenkiste, Karol Kurach, Raphaël Marinier, Marcin Michalski, and Sylvain Gelly.
FVD: A new metric for video generation, 2019.
URL [https://openreview.net/forum?id=rylgEULtdN](https://openreview.net/forum?id=rylgEULtdN).
- Wan et al. (2025)
Team Wan, Ang Wang, Baole Ai, Bin Wen, Chaojie Mao, Chen-Wei Xie, Di Chen, Feiwu Yu, Haiming Zhao, Jianxiao Yang, Jianyuan Zeng, Jiayu Wang, Jingfeng Zhang, Jingren Zhou, Jinkai Wang, Jixuan Chen, Kai Zhu, Kang Zhao, Keyu Yan, Lianghua Huang, Mengyang Feng, Ningyi Zhang, Pandeng Li, Pingyu Wu, Ruihang Chu, Ruili Feng, Shiwei Zhang, Siyang Sun, Tao Fang, Tianxing Wang, Tianyi Gui, Tingyu Weng, Tong Shen, Wei Lin, Wei Wang, Wei Wang, Wenmeng Zhou, Wente Wang, Wenting Shen, Wenyuan Yu, Xianzhong Shi, Xiaoming Huang, Xin Xu, Yan Kou, Yangyu Lv, Yifei Li, Yijing Liu, Yiming Wang, Yingya Zhang, Yitong Huang, Yong Li, You Wu, Yu Liu, Yulin Pan, Yun Zheng, Yuntao Hong, Yupeng Shi, Yutong Feng, Zeyinzi Jiang, Zhen Han, Zhi-Fan Wu, and Ziyu Liu.
Wan: Open and advanced large-scale video generative models.
*arXiv preprint arXiv:2503.20314*, 2025.
- Wang et al. (2024)
Weimin Wang, Jiawei Liu, Zhijie Lin, Jiangqiao Yan, Shuo Chen, Chetwin Low, Tuyen Hoang, Jie Wu, Jun Hao Liew, Hanshu Yan, et al.
Magicvideo-v2: Multi-stage high-aesthetic video generation.
*arXiv preprint arXiv:2401.04468*, 2024.
- Wang & Yang (2024)
Wenhao Wang and Yi Yang.
Vidprom: A million-scale real prompt-gallery dataset for text-to-video diffusion models, 2024.
URL [https://arxiv.org/abs/2403.06098](https://arxiv.org/abs/2403.06098).
- Wang et al. (2025a)
Yiping Wang, Xuehai He, Kuan Wang, Luyao Ma, Jianwei Yang, Shuohang Wang, Simon Shaolei Du, and Yelong Shen.
Is your world simulator a good story presenter? a consecutive events-based benchmark for future long video generation.
In *Proceedings of the Computer Vision and Pattern Recognition Conference*, pp. 13629–13638, 2025a.
- Wang et al. (2025b)
Zeqing Wang, Qingyang Ma, Wentao Wan, Haojie Li, Keze Wang, and Yonghong Tian.
Is this generated person existed in real-world? fine-grained detecting and calibrating abnormal human-body.
In *Proceedings of the Computer Vision and Pattern Recognition Conference*, pp. 21226–21237, 2025b.
- Wang et al. (2025c)
Zeqing Wang, Wentao Wan, Qiqing Lao, Runmeng Chen, Minjie Lang, Xiao Wang, Keze Wang, and Liang Lin.
Towards top-down reasoning: An explainable multi-agent approach for visual question answering, 2025c.
URL [https://arxiv.org/abs/2311.17331](https://arxiv.org/abs/2311.17331).
- Wei et al. (2025)
Xinyu Wei, Jinrui Zhang, Zeqing Wang, Hongyang Wei, Zhen Guo, and Lei Zhang.
Tiif-bench: How does your t2i model follow your instructions?, 2025.
URL [https://arxiv.org/abs/2506.02161](https://arxiv.org/abs/2506.02161).
- Wikipedia (2025a)
Wikipedia.
Giant panda — Wikipedia, the free encyclopedia.
[http://en.wikipedia.org/w/index.php?title=Giant%20panda&oldid=1310265925](http://en.wikipedia.org/w/index.php?title=Giant%20panda&oldid=1310265925), 2025a.
[Online; accessed 10-September-2025].
- Wikipedia (2025b)
Wikipedia.
Longest common subsequence — Wikipedia, the free encyclopedia.
[http://en.wikipedia.org/w/index.php?title=Longest%20common%20subsequence&oldid=1307980713](http://en.wikipedia.org/w/index.php?title=Longest%20common%20subsequence&oldid=1307980713), 2025b.
[Online; accessed 17-September-2025].
- Wikipedia (2025c)
Wikipedia.
Sulfuric acid — Wikipedia, the free encyclopedia.
[http://en.wikipedia.org/w/index.php?title=Sulfuric%20acid&oldid=1310387905](http://en.wikipedia.org/w/index.php?title=Sulfuric%20acid&oldid=1310387905), 2025c.
[Online; accessed 10-September-2025].
- Xiao et al. (2025)
Junfei Xiao, Ceyuan Yang, Lvmin Zhang, Shengqu Cai, Yang Zhao, Yuwei Guo, Gordon Wetzstein, Maneesh Agrawala, Alan Yuille, and Lu Jiang.
Captain cinema: Towards short movie generation.
*arXiv preprint arXiv:2507.18634*, 2025.
- Yang et al. (2024)
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.
- Yoon et al. (2024)
Jaehong Yoon, Shoubin Yu, and Mohit Bansal.
Raccoon: Remove, add, and change video content with auto-generated narratives.
*arXiv:2405.18406*, 2024.
- Zhang et al. (2024)
Renrui Zhang, Xinyu Wei, Dongzhi Jiang, Ziyu Guo, Shicheng Li, Yichi Zhang, Chengzhuo Tong, Jiaming Liu, Aojun Zhou, Bin Wei, Shanghang Zhang, Peng Gao, Chunyuan Li, and Hongsheng Li.
Mavis: Mathematical visual instruction tuning with an automatic data engine, 2024.
URL [https://arxiv.org/abs/2407.08739](https://arxiv.org/abs/2407.08739).
- Zheng et al. (2025)
Dian Zheng, Ziqi Huang, Hongbo Liu, Kai Zou, Yinan He, Fan Zhang, Yuanhan Zhang, Jingwen He, Wei-Shi Zheng, Yu Qiao, et al.
Vbench-2.0: Advancing video generation benchmark suite for intrinsic faithfulness.
*arXiv preprint arXiv:2503.21755*, 2025.
- Zheng et al. (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.
*arXiv preprint arXiv:2412.20404*, 2024.

## Appendix A The Pipeline of Prompt Construction

As illustrated in Fig. [4](https://arxiv.org/html/2510.08398v2#A1.F4), the construction pipeline of our prompts begins with three source domains: VidProM (science fiction), ActivityNet (daily life), and web-collected high school level experiments. Each domain undergoes domain-specific processing to generate raw-prompt pools. Subsequently, GPT-4o is employed to rewrite these prompts and extract temporally related events. Finally, independent annotators refine the outputs by incorporating one or more evaluation dimensions while preserving the original event structure, resulting in the final prompts used in VideoVerse.

Figure: Figure 4: Prompt construction pipeline of VideoVerse. Source prompts are drawn from three domains: science fiction (VidProM), daily life (ActivityNet), and human collected high school level experiments. After domain specific filtering, GPT-4o extracts temporally related events to form raw prompts. Independent annotators then refine these raw prompts by incorporating one or more evaluation dimensions, while preserving the original event structure, to produce the final prompts.
Refer to caption: x4.png

## Appendix B Evaluation Prompts

### B.1 Binary Evaluation Question

In VideoVerse, all evaluation dimensions, except Event Following, are assessed using binary questions. The prompts for these questions are listed in Tab. [4](https://arxiv.org/html/2510.08398v2#A2.T4). After obtaining the VLM’s response, we extract the final answer (“Yes” or “No”) by regular expressions.

Table: Table 4: Prompt for Binary Question Evaluation used in our VideoVerse.

### B.2 Event Evaluation Question

For the Event Following dimension, the corresponding evaluation prompt is shown in Tab. [5](https://arxiv.org/html/2510.08398v2#A2.T5). To ensure robust parsing and avoid ambiguity in free-form responses, we instruct the VLM to enclose its output within $\langle$output$\rangle$ and $\langle$/output$\rangle$ tags. The enclosed content is then extracted using regular expressions for evaluation.

Table: Table 5: Prompt for Event Following evaluation used in our VideoVerse. The VLMs (e.g., Gemini2.5 Pro) respond with the existing events’ index in order, which is then used to calculate the LCS with the ground truth event order.

## Appendix C Evaluation Details

### C.1 Open-Source T2V Models

For the open-source T2V models evaluated in this paper, all experiments are conducted on servers with NVIDIA A800 GPUs. The sources of the T2V models are as follows:

- •
CogVideoX1.5: accessed from [CogVideoX Official GitHub](https://github.com/THUDM/CogVideo).
- •
OpenSora2.0: accessed from [OpenSora Official GitHub](https://github.com/hpcaitech/Open-Sora).
- •
SkyReels-V2: accessed from [SkyReels-V2 Official GitHub](https://github.com/SkyworkAI/SkyReels-V2).
- •
Wan2.1-14B: accessed from [Wan2.1-14B Official GitHub](https://github.com/Wan-Video/Wan2.1), with checkpoints and inference code from [Hugging Face](https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-720P).
- •
Wan2.2-A14B: accessed from [Wan2.2-A14B](https://github.com/Wan-Video/Wan2.2), with checkpoints and inference code from [Hugging Face](https://huggingface.co/Wan-AI/Wan2.2-T2V-A14B).
- •
Hunyuan: accessed from [Hunyuan Official GitHub](https://github.com/Tencent-Hunyuan/HunyuanVideo).

Furthermore, as discussed in Sec. [1](https://arxiv.org/html/2510.08398v2#S1) of the main paper, the rapid development of T2V models has led to a significant increase in inference time. We report the video inference time along with the GPUs used for each model in Tab. [6](https://arxiv.org/html/2510.08398v2#A3.T6). We can see that it will take 648,000 seconds (about one week) to finish the inference of Skyreels-V2(L) on VideoVerse (300 prompts). This also motivates the design of our VideoVerse, where multiple evaluation dimensions are incorporated for each prompt to better capture the trade-offs in efficiency and evaluation.

### C.2 Closed-Source T2V Models

For closed-source models, we access them exclusively through their official APIs: Minimax-Hailuo via (^1^11https://www.minimax.io/platform/document/video_generation), Veo-3 via (^2^22https://developers.googleblog.com/en/veo-3-now-available-gemini-api/) and Sora-2 via(^3^33https://api.openai.com/v1/videos). Due to the high cost of Veo-3 and Sora-2, we employ Veo-3-fast and Sora-2 standard versions in our experiments.

Table: Table 6: Single video inference time and the used GPUs of open-source T2V models.

Table: Table 7: Statistics of our VideoVerse. The light gray rows represent the world model level dimensions, while the other rows represent the basic level dimensions.

## Appendix D Statistics of VideoVerse

### D.1 Statistics of Each Evaluation Dimension

Tab. [7](https://arxiv.org/html/2510.08398v2#A3.T7) summarizes the detailed statistics of VideoVerse. The VideoVerse includes six world model level evaluation dimensions: Material Properties, Natural Constraints, Common Sense, Mechanics, Interaction, and Event Following, which are designed to assess T2V models from a world model perspective. In addition, VideoVerse incorporates four basic level dimensions: Attribute Correctness, 2D Layout, 3D Depth, and Camera Control, which aim to evaluate the fundamental abilities of a T2V model. Among them, Camera Control is particularly challenging as it requires strong instruction-following capability.

### D.2 The Design of “Hidden Semantics”

As emphasized in Sec. [1](https://arxiv.org/html/2510.08398v2#S1) of the main paper, a key design of our VideoVerse is the “hidden semantics” within the prompts. To illustrate this, we compare VideoVerse with the most recent T2V benchmark, VBench2.0. As shown in Tab. [8](https://arxiv.org/html/2510.08398v2#A4.T8), VBench2.0 often incorporates explicit descriptions of physical phenomena in the prompt itself (e.g., specifying that a water droplet remains “spherical” due to surface tension). However, such details should not be explicitly provided in the prompt, as they should be inferred by the T2V model as world knowledge. In contrast, VideoVerse intentionally hides these semantics within the prompt, thereby requiring models to infer and generate them based on their learned world modeling ability, rather than textual guidance.

**Table 8: Comparison of prompts between VBench2.0 and our VideoVerse. Unlike VBench2.0, which explicitly encodes physical outcomes in the prompt, VideoVerse introduces “hidden semantics” elements. This design forces T2V models to rely on their intrinsic world knowledge to generate implicit but necessary phenomena, enabling a more faithful evaluation of the world modeling ability.**
| \rowcolorgray!15<br>VBench2.0 (Mechanics) | A water droplet slides down the smooth surface of a marble countertop, remaining spherical due to surface tension. |
| --- | --- |
| \rowcolorgray!15<br>VBench2.0 (Mechanics) | A soft rubber duck is tossed onto the floor, showing its energetic bounce as it strikes the surface. |
| \rowcolorblue!8<br>VideoVerse | As a sleek, futuristic spaceship with reflective silver plating sails across Jupiter. Then the spaceship maneuvers gracefully through a dense cluster of asteroids and dodges asteroids with precision. |
| \rowcolorblue!8 | Hidden Semantics: The surface of the planet in the video has colorful striped patterns. |
| \rowcolorblue!8 | Evaluation (Natural Constraints): Does the surface of the planet in the video have colorful striped patterns? |
| \rowcolorblue!8<br>VideoVerse | An athlete wearing a red vest competes in the long jump. He sprints toward the left side of the screen and takes off, leaping high into the air and stretching his legs forward as far as possible, before landing in the sandpit on his hips. He then gets up and walks away. |
| \rowcolorblue!8 | Hidden Semantics: After the athlete lands, there is a pit in the sand. |
| \rowcolorblue!8 | Evaluation (Interaction): After the athlete lands, is there a pit in the sand? |

### D.3 Temporal Causality of “Event Following”

Another important design of our VideoVerse is that every prompt is constructed based on at least one event. For prompts involving multiple events, we emphasize their temporal causality in most cases, aligning with our LCS-based evaluation method. As shown in Tab. [9](https://arxiv.org/html/2510.08398v2#A4.T9), the three events form a strict temporal chain that cannot be reordered: if the man does not throw the frisbee, the dog cannot fetch it; if the dog does not fetch it back, the man cannot leash the dog; and once the man leashes the dog, the dog cannot fetch the frisbee. This fixed and unique order highlights the temporal causality explicitly embedded in our prompts. It is worth noting that not all prompts follow this rule; for example, in the Science Fiction category, we deliberately relax temporal causality to encourage model creativity.

**Table 9: An example of event-based prompt design in VideoVerse. The prompt consists of three causal events (A, B, C), which must occur in a fixed order to preserve temporal causality, consistent with our LCS-based evaluation method.**
| Prompt | A man plays fetch with a golden retriever on a grassy field. Standing on the left side of the frame, he throws a frisbee to the right, and the dog runs out to retrieve it. The man then takes out a leash and attaches it to the dog. |
| --- | --- |
| \rowcolorblue!8<br>Event A | A man throws a frisbee. |
| \rowcolorblue!8<br>Event B | The dog fetches the frisbee back to the man. |
| \rowcolorblue!8<br>Event C | The man leashes his dog. |
| Temporal Causality | The order of events (A $\rightarrow$ B $\rightarrow$ C) is fixed and unique:<br>If A does not occur, B cannot occur;<br>If B does not occur, C cannot occur;<br>Once C occurs, B cannot follow. |

### D.4 Scene Coverage

To further assess whether VideoVerse can serve as a benchmark for evaluating the world model capabilities of T2V models, we extract the potential scenes implied by prompts like “kitchen” or “playground” in VideoVerse and several other mainstream T2V benchmarks via GPT-4o. We then measure the scene uniqueness of each benchmark. As shown in Fig. [5](https://arxiv.org/html/2510.08398v2#A4.F5), VideoVerse achieves higher uniqueness by ensuring that each prompt corresponds to a distinct scene (as the 100% base). In contrast, prompts in other benchmarks are often repetitive, overly simple, or loosely related to specific scenes, resulting in lower scene uniqueness.

Figure: Figure 5: Comparison of scene uniqueness across T2V benchmarks. Scenes implied by prompts are extracted using GPT-4o, and semantic embeddings are used to merge similar scenes. VideoVerse achieves the highest uniqueness, ensuring broader and more diverse scene coverage.
Refer to caption: x5.png

## Appendix E More Case Studies

### E.1 The Gap between T2V Models and a “World Model”

As emphasized in Sec. [4.2](https://arxiv.org/html/2510.08398v2#S4.SS2) of the main paper, although current SOTA T2V models demonstrate certain abilities of a “World Model”, there still exists a significant gap. We illustrate this with two cases in Fig. [6](https://arxiv.org/html/2510.08398v2#A5.F6). In Fig. [6(a)](https://arxiv.org/html/2510.08398v2#A5.F6.sf1), Minmax-Hailuo successfully generates the content of “a man shaving his beard”, but the beard remains unchanged. This indicates that the model fails to capture the implicit world knowledge that “shaving a beard” also implies that “the beard should disappear”, which is related to Interaction. In Fig. [6(b)](https://arxiv.org/html/2510.08398v2#A5.F6.sf2), Hunyuan correctly generates dry ice and places it in the right location, but it fails to produce the vapor that should appear when dry ice is exposed to room temperature. This reveals that the model does not understand the physical knowledge that dry ice rapidly sublimates into vapor at room temperature.

Figure: (a) (a) Hailuo Video can generate appealing man shaving actions, but it fails on the dimension of Interaction: while the razor repeatedly moves across the beard, the beard remains unchanged.
Refer to caption: x6.png

### E.2 More Cases in ViderVerse

Fig. [8](https://arxiv.org/html/2510.08398v2#A7.F8) presents more cases of VideoVerse, covering different evaluation dimensions and prompt types, including Scientific Experiment and Science Fiction. By integrating carefully designed evaluation dimensions into the prompts, VideoVerse provides a comprehensive assessment of current T2V models’ capabilities from a world model perspective.

## Appendix F Details of User Study

Participant Selection and Answer Collection. Since VideoVerse aims to evaluate T2V models from a world model perspective, participants in the user study are required to have a certain level of knowledge. Following the same protocol as our data annotation process, all participants hold at least a bachelor’s degree, with some at the PhD level. We provide the user interface of our user study in Fig. [7](https://arxiv.org/html/2510.08398v2#A6.F7). Each participant is presented with a video without its corresponding prompt, and is asked to answer a binary question related to the video, without being informed of the associated evaluation dimension. Finally, participants are asked to order the events that occurred in the video. To ensure data quality, participants cannot submit their answers until the video is played.

Results of User Study. After collecting the responses from all participants, binary questions are evaluated by directly computing the proportion of answers consistent with Gemini 2.5 Pro. For Event Following, we measure consistency by calculating the longest common subsequence (LCS, see Sec. [3.4](https://arxiv.org/html/2510.08398v2#S3.SS4) in the main paper) between Gemini’s response and each participant’s response, and then aggregating by multiplying Gemini’s LCS score with the number of participants to yield a scale-adjusted consistency ratio. As shown in Tab. [10](https://arxiv.org/html/2510.08398v2#A6.T10), most evaluation categories exhibit a high consistency ratio ($>$90%). However, the Camera Control exhibits the lowest consistency ratio. This is because its changes typically occur throughout the entire video, making it more difficult for the VLM to understand and requiring more granular participant observation of the video (e.g., focus control is typically only reflected in a certain area of the video). For the Natural Constraints, since interpreting these phenomena often requires specific scientific knowledge and domain expertise, and the underlying cues are sometimes subtle, which are difficult for the VLM. This also motivates us to require that all prompt annotators and user study participants should hold at least a bachelor’s degree. For other evaluation dimensions, consistency ratios in dynamic tasks (e.g., Mechanics and Camera Control) are slightly lower than those in static ones, as answering these questions demands temporal reasoning, which is inherently more difficult. For 2D Layout, inconsistencies mainly arise from object occlusions in the video, which can lead to confusion in spatial descriptions such as distinguishing between “left” and “right”.

Figure: Figure 7: Interface used for the user study. The participant can watch the video, but cannot see the prompts that are used to generate the video, nor do they know which model generates it. The participant is required to answer the Basic Questions, i.e., the binary evaluation questions and then the Event Following dimension. They also need to complete the Order Selection, arranging a series of events in order; if they believe a certain event does not occur, they can select “null”.
Refer to caption: x8.png

Table: Table 10: Detailed User Study Results.

## Appendix G Other VLM as Evaluator

Table: Table 11: Performance of Open-Source and Closed-Source T2V models on VideoVerse based on the Open-Source VLM Qwen2.5-VL 32B, with the best values highlighted in bold. The light green columns represent the world model level dimensions, while the other columns represent the basic level dimensions.

Figure: Figure 8: More examples of different T2V models in our VideoVerse.
Refer to caption: x9.png

Although the powerful Gemini 2.5 Pro demonstrates high consistency with human evaluations, it remains a closed-source commercial model, which limits its accessibility for the community to use as an evaluator for their own T2V models. With the increasing advancement of open-source VLM approachesZhang et al. (2024); Lin et al. (2025a; c); Tong et al. (2025); Lin et al. (2025b; 2023); An et al. (2025b; a); Luo et al. (2024); Wang et al. (2025c), we explore the use of open-source VLMs as evaluators. Tab. [11](https://arxiv.org/html/2510.08398v2#A7.T11) reports the evaluation results when adopting Qwen2.5-VL 32B as the evaluator (considering the balance between performance and efficiency). To assess whether it can serve as a substitute for Gemini 2.5 Pro used in our main results, we first calculate the Spearman correlation coefficient with the results in Tab. [1](https://arxiv.org/html/2510.08398v2#S4.T1), obtaining a value of $0.71$. This indicates that there is still a gap between the two evaluation results. The most significant differences occur in the basic static dimensions, i.e., Attribute Correctness, 2D Layout, and 3D Depth. A closer analysis reveals that for 2D Layout and 3D Depth, the spatial relationships between objects are often affected by occlusion, which substantially increases the difficulty for open-source VLMs with limited video understanding ability. For Attribute Correctness, the discrepancy arises because the evaluated objects do not remain consistently in prominent positions throughout the video, leading to divergence from Gemini 2.5 Pro’s evaluations.

For overall performance, the difference between Qwen2.5-VL 32B and Gemini 2.5 Pro is not substantial. Based on the LCS metric described in Sec. [3.4](https://arxiv.org/html/2510.08398v2#S3.SS4) of the main paper, the overall ranking difference between Tab. [11](https://arxiv.org/html/2510.08398v2#A7.T11) (results from Qwen2.5-VL 32B) and Tab. [1](https://arxiv.org/html/2510.08398v2#S4.T1) (results from Gemini 2.5 Pro) is $3$. Moreover, models with changes in relative positions are already very close in performance, such as CogVideoX1.5 (L) vs. CogVideoX1.5 (S) and SkyReels-V2 (S) vs. SkyReels-V2 (L). These results suggest that current open-source VLMs can serve as evaluators for VideoVerse, as the overall rankings are very close without altering the conclusions of VideoVerse. Nonetheless, since our comprehensive user study has confirmed the high alignment of Gemini 2.5 Pro with human evaluations, and given that open-source VLMs still exhibit certain discrepancies compared to Gemini 2.5 Pro on VideoVerse, we recommend using the more powerful Gemini 2.5 Pro as the primary evaluator for VideoVerse.

## Appendix H Usage of LLMs in This Paper

In this work, Large Language Models (LLMs) are employed for stylistic refinement, polishing the manuscript and processing the data. All original content and ideas are conceived and written by the authors.

## Peer Reviews

confidence: 5
contribution: 2
initial_rating: 2
presentation: 3
questions: See weakness.
soundness: 2
strengths: 1. This paper introduces VideoVerse, the first benchmark designed to specifically evaluate T2V models from a "world model" perspective. This fills a critical gap, as existing benchmarks are insufficient for assessing complex temporal causality and world knowledge.
2. The benchmark employs a novel "hidden semantics" prompt design. This requires models to infer and generate realistic outcomes based on physical laws or common sense (e.g., "baking soda and vinegar" should produce "bubbles") , testing deeper reasoning rather than just explicit instruction-following.
summary: This paper introduces VideoVerse, a new benchmark to test if T2V (Text-to-Video) models can function as "world models". It uses 300 prompts with "hidden semantics" to evaluate complex temporal causality and world knowledge, which current benchmarks miss. The study finds that all T2V models, especially open-source ones, still significantly lack these advanced reasoning abilities and are far from true world models.
weaknesses: 1. The definition of "world model" in this paper is not clear. It seems "world model" here refers to T2V models that can work well on a set of pre-defined dimensions. However, a world model is expected to face an "open world," and it is questionable whether its capabilities can be adequately measured by a small number of fixed dimensions.

2. The benchmark is predicated on the assumption that Gemini 2.5 Pro (the evaluator) is already a "world model" that understands the world. However, the authors provide no literature to support this claim. Furthermore, the user study was conducted using only 15 videos, and an experiment with such a small dataset is unconvincing.

3. The number of prompts is scarce. Although the authors emphasize that the 300 prompts are carefully designed, the scope of "world knowledge" and "natural laws" is extremely vast. These 300 prompts likely represent only a very small subset of this knowledge, which limits the complexity of the natural constraints being tested.

---

confidence: 3
contribution: 3
initial_rating: 4
presentation: 3
questions: See weaknesses.
soundness: 2
strengths: 1. The core premise of the paper is timely and important. As T2V models improve in fidelity, the field urgently needs benchmarks that push beyond superficial quality metrics and test deeper understanding. The focus on "world models" aligns with a critical long-term goal for AI.

2. The use of a powerful VLM (Gemini 2.5 Pro) for automated evaluation, combined with a well-defined cumulative scoring strategy and a specific protocol (LCS for event following, binary questions for other dimensions), is robust. The user study validating the VLM's judgments (>90% consistency) is essential for establishing the credibility of the automated evaluation method.
summary: This paper introduces VideoVerse, a novel benchmark designed to evaluate Text-to-Video (T2V) models from the perspective of a "world model." The authors argue that existing benchmarks, which focus on frame-level quality and explicit semantic alignment, are insufficient for assessing state-of-the-art models that are beginning to demonstrate rudimentary reasoning about temporal causality and real-world knowledge. VideoVerse addresses this gap with a carefully curated set of 300 prompts that require models to infer "hidden semantics" and adhere to physical laws and common sense. The benchmark is evaluated using a VLM-based QA pipeline, and a comprehensive analysis of leading open-source and closed-source models reveals significant gaps, particularly in world-modeling capabilities.
weaknesses: 1. While the paper acknowledges that the definition of a "world model" is multi-faceted, VideoVerse primarily tests physical and commonsense reasoning. Other critical aspects of a world model, such as theory of mind, long-term planning, complex social dynamics, or counterfactual reasoning, are not addressed. 

2. The entire evaluation hinges on the capabilities and biases of Gemini 2.5 Pro. Although the user study shows high alignment, it is limited in scale (15 videos, 11 participants). Potential biases in the VLM (e.g., towards certain visual concepts or narrative structures) could propagate into the benchmark scores. 

3. The multi-stage pipeline involving GPT-4o and human annotators, while ensuring quality, may limit the scalability and reproducibility of the benchmark. A more detailed analysis of inter-annotator agreement during the prompt refinement stage would strengthen the methodology.

---

confidence: 3
contribution: 3
initial_rating: 6
presentation: 3
questions: Are the prompts public and reusable for benchmarking future T2V systems?
soundness: 2
strengths: - Addresses an important and timely problem. The paper clearly identifies that existing benchmarks (VBench, EvalCrafter, StoryEval) fail to capture causal and world knowledge reasoning. The emphasis on temporal causality between events and implicit physical/chemical knowledge (like dry ice sublimation, acid-base reactions) is a genuinely useful direction as these models become more sophisticated.
- Comprehensive and well-structured benchmark. (1) The paper evaluates 8 different models (both open and closed source) and provides detailed breakdowns across all 10 dimensions covering both static and dynamic reasoning. The results clearly show that even SOTA models like Veo-3 only achieve 76.4% on world model dimensions vs 81.3% on basic dimensions, which nicely demonstrates the gap. (2) The case studies effectively illustrate where models fail, making the findings concrete and interpretable. (3) I like how they connect low-level perceptual factors (camera control, 2D layout) with higher-level “world understanding” factors like natural constraints and interaction. This hierarchical setup makes the benchmark more realistic and nuanced. (4) The authors validate Gemini 2.5 Pro’s consistency with human judgments (>90% agreement), which adds credibility to the evaluation.
summary: This paper introduces VideoVerse, a new benchmark for evaluating text-to-video (T2V) models from a "world model" perspective. The benchmark consists of 300 carefully curated prompts with 815 events and 825 binary evaluation questions across 10 dimensions (5 dynamic, 5 static). The key innovation is the focus on "hidden semantics" - requiring models to generate content based on implicit world knowledge rather than just following explicit textual instructions. The authors evaluate several open-source and closed-source T2V models using Gemini 2.5 Pro as the evaluator and conduct a user study showing high alignment with human judgment.
weaknesses: - The “world model” claim is a bit overstated. The title and intro heavily emphasize world models, but the benchmark only probes limited prompt coverage and short-duration videos (5 - 10 seconds). 300 prompts with specific evaluation dimensions can't really capture the full scope of what a world model should understand. The 10 dimensions are useful but can be further extended. There's no evaluation of prediction, simulation, social interactions, long-term planning, longer-term consistency, or many other aspects of world understanding. So it feels more like a semantic-causal benchmark than a true world-model testbed.
- Missing important baseline models and limited accessibility of evaluator. The paper doesn't include Kling, Sora, or Pika which are prominent T2V models frequently discussed in the community. The absence of Sora is particularly glaring given OpenAI's explicit positioning of it as a world simulator. While I understand there may be API access issues, this limits the completeness of the evaluation. Additionally, the reliance on Gemini 2.5 Pro (a closed-source commercial model) as the primary evaluator creates accessibility barriers. Table 11 shows Qwen2.5-VL has only 0.71 Spearman correlation with Gemini, and the authors still recommend using Gemini, which means researchers without API access can't reliably use this benchmark.
- Reliance on GPT-4o for prompt construction and annotation may introduce bias. Section 3.3 mentions using GPT-4o heavily for filtering, causality extraction, and even dimension selection. This automated pipeline could bias the dataset toward GPT-style reasoning patterns, but there’s no quantitative check of annotation diversity.