Is Sora a world simulator? The world’s first comprehensive review analyzes the universal world model.

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A world model, namely understanding the digital and physical world by predicting the future, is one of the key paths to achieving Artificial General Intelligence (AGI).
In the field of video generation, Sora, released by OpenAI, has attracted wide attention due to its strong simulation ability, showing a preliminary understanding of the physical world. Leading video generation company Runway stated in its technical blog that the next-generation product of the Gen-2 life-video system will be realized through the general world model. In the field of autonomous driving, both Tesla and Wayve have stated that they are building their end-to-end autonomous driving systems using the future prediction features of the world model. In the broader field of general robot intelligence, LeCun has repeatedly expressed great interest in the potential of world models in his speeches, predicting that world models will replace autoregressive models as the basis for next-generation intelligent systems.
To comprehensively explore and summarize the latest progress of the world model, researchers from Beijing Excellent Vision Technology Co., Ltd. (Excellent Technology) jointly launched the world’s first comprehensive review of the general world model with several domestic and foreign institutions (Institute of Automation, Chinese Academy of Sciences, National University of Singapore, Institute of Computing Technology, Chinese Academy of Sciences, Shanghai Artificial Intelligence Laboratory, Mychi, Northwestern Polytechnical University, Tsinghua University, etc.).
This review, based on more than 260 documents, provides a detailed analysis and discussion of world model research and applications in fields such as video generation, autonomous driving, intelligent entities, and general robots. In addition, the review also looks at the current challenges and limitations of world models and looks forward to their future development.
Researchers from Excellent Technology said they would continue to update more research progress on the general world model in the GitHub project, hoping that the review could serve as a research reference for the general world model.
Is Sora a world simulator? The world's first comprehensive review analyzes the universal world model.
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World models enhance understanding of the world through predictions of the future. As shown in the figure below, the predictive capability of world models is crucial for the development of video generation, autonomous driving, and intelligent entities, which represent the mainstream application directions of world model research.

Firstly, a video generation world model refers to the use of world model technology to generate and edit videos, so as to understand and simulate real-world scenarios. This way, complex visual information can be better understood and expressed, providing new possibilities for artistic creation.

Secondly, an autonomous driving world model refers to the use of video generation and prediction technology to create and understand driving scenarios, and to learn driving behaviors and strategies from these scenarios, which is important for implementing end-to-end autonomous driving systems.

Lastly, an intelligent entity world model refers to the use of video generation and prediction technology to establish interactions between intelligent entities and the environment in dynamic environments. Different from the autonomous driving model, the intelligent entity world model builds an intelligent strategy network suitable for various environments and situations; these intelligent entities may be virtual, such as controlling character behavior in games, or they may be physical, such as controlling robots to perform tasks in the physical world. In this way, intelligent entity world models provide new solutions for intelligent interaction and intelligent control.

Is Sora a world simulator? The world's first comprehensive review analyzes the universal world model.

Field of Video Generation

As shown in the figure below, this review first discusses the representative video generation models proposed in recent years in chronological order. Prior to 2021, models based on GAN (IRC-GAN, TGANs-C, TFGSN, StoryGAN, TiVGAN etc.) dominated the field of video generation. Subsequently, models based on autoregressive modeling (GODIVA, VideoGPT etc.), diffusion modeling (Imagen Video, SVD, CogVideo etc.) and masked modeling (MAGVIT, VideoPoet, WorldDreamer etc.) began to emerge, achieving better generation results.
Is Sora a world simulator? The world's first comprehensive review analyzes the universal world model.
Models based on GAN (Figure (a) below) conduct adversarial training of the generator and discriminator networks to make the generated videos more realistic. Diffusion modeling (Figure (b) below) encodes the video signal to the latent space and introduces a denoising process to generate videos. High-quality videos are decoded and recovered from pure noise through multi-step denoising in latent space. Models based on autoregressive modeling (Figure (c) below) use the method of predicting the next visual Token to gradually generate the video content corresponding to the next time. This method can capture the dependencies in the time series, and generate coherent and realistic videos. Masked modeling (Figure (d) below) obscures some visual information during training and gradually recovers the masked areas, ultimately resulting in clear videos without masks. In summary, video generation models in recent years have shown a trend towards diversification and innovation, with different model methods successively emerging and achieving impressive generation results.
Is Sora a world simulator? The world's first comprehensive review analyzes the universal world model.
Sora is a highly acclaimed video generation model recently, whose technical scheme relies on the diffusion model shown in Figure (c) above. Since Sora is closed source, the relevant analysis in this review is mainly based on its technical report ( As shown in the figure below, Sora mainly includes three parts:

  1. Compression model: This model compresses the original video in time and space, converts it into hidden space features for representation, and has a decoder that can map hidden space features back to the original video.
  2. Transformer-based Diffusion Model: Similar to DiT (Scalable Diffusion Models with Transformers) method, this model continuously reduces the noise of visual features in the latent space.
  3. Language model: The large language model is used to encode the user’s inputs into detailed promts to control the generation of the video.
Is Sora a world simulator? The world's first comprehensive review analyzes the universal world model.

Autonomous driving field

In addition to video generation, world models also have great application value in the field of autonomous driving, which has been continuously explored by researchers in recent years. The figure below shows the development of world model research in autonomous driving scenarios since 2023, which includes three types: end-to-end autonomous driving, 2D driving scene simulators, and 3D driving scene simulators.
Is Sora a world simulator? The world's first comprehensive review analyzes the universal world model.
In the field of autonomous driving, world models can be used to construct a dynamic representation of the environment. Accurate prediction of the future is crucial for ensuring safe driving under various conditions. Therefore, end-to-end autonomous driving methods (Iso-Dream, MILE, SEM2, etc.) address these challenges by minimizing the search space and integrating visual dynamics in a clear decoupling on the CARLA v1 simulator. TrafficBots is another end-to-end driving method based on world models that focuses on predicting the behavior of each intelligent agent in a given scenario. Considering the destination of each agent, TrafficBots uses conditional variational autoencoders to assign unique features to each agent, predicting actions from a BEV (Bird’s Eye View) perspective.

The above methods were experimented on CARLA v1, but face the challenge of data inefficiency in CARLA v2. To deal with the complexity of CARLA v2 scenes, Think2Drive proposes a model-based reinforcement learning method for autonomous driving, which encourages the planner to “think” in the learned latent space. This method significantly improves training efficiency by utilizing low-dimensional state space and parallel computing tensors.

High-quality data is the cornerstone of training deep learning models. Although internet text and image data are low-cost and easy to acquire, there are many challenges in acquiring data in the field of autonomous driving, including the complexity of sensors and privacy issues, especially when acquiring long-tail targets that directly affect actual driving safety. World models are crucial for understanding and simulating the complex physical world.

Some recent research has introduced diffusion models into the field of autonomous driving to construct world models as neural simulators, generating the necessary autonomous 2D driving videos. In addition, some methods use world models to generate 3D occupancy grids or LiDAR point clouds of future scenes.

The table below provides a summary of the driving scene data generation methods based on world models.

Is Sora a world simulator? The world's first comprehensive review analyzes the universal world model.

Intelligent agent and robotics field

In addition to the field of autonomous driving, world models have a wide range of applications in the fields of intelligent agents and robotics. Given a task, an intelligent agent needs to plan a series of actions to complete the assigned task. There have been many successful algorithms for dynamic planning in known environments, however, in most cases, the environment is complex and random, and difficult to model explicitly through human experience.

Therefore, a central topic in this field is how intelligent agents can learn to plan in unknown and complex environments. One way to solve this problem is to allow the intelligent agent to accumulate experience from interactions with the environment, and learn behavior directly from the experience without modeling the state changes of the environment (i.e., model-free reinforcement learning). Although this solution is simple and flexible, the learning process depends on many interactions with the environment, which is very costly.

World Models is the first research to introduce the concept of world models in the field of reinforcement learning. It models the knowledge of the world from the agent’s experience and gains the ability to predict the future. This work suggests that even a simple recurrent neural network model can capture the dynamic information of the environment and support the agent in learning and evolving strategies in the model. This learning paradigm is called “learning in the imagination”. With a world model, the cost of experiments and failures can be significantly reduced.

The diagram below provides an overview of the development of world models in the field of intelligent agents and robotics, with different colors indicating different structures of world models. Where the RSSM (PlatNet, DreamerV1, DreamerV2, DreamerV3, etc.) dominates, while the Transformer (TransDreamer, IRIS, Genie, etc.), JEPA (JEPA, MC-JEPA, A-JEPA, V-JEPA, etc.) and diffusion models (RoboDreamer, UniSim) have received increasing attention since 2022.

Is Sora a world simulator? The world's first comprehensive review analyzes the universal world model.
The Recurrent State Space Model (RSSM) is the core structure of the Dreamer series, aimed at facilitating prediction in the latent space. It learns the dynamic model of the environment from pixel observations and selects actions by planning in the encoded latent space. By decomposing the latent state into stochastic and deterministic parts, this model takes into account the deterministic and random factors of the environment. Due to its excellent performance in continuous control tasks of robots, many subsequent works have been expanded based on it.

Joint-Embedding Predictive Architecture (JEPA) was proposed by LeCun et al., and learns the mapping relationship from input data to predicted output. Unlike traditional generative models, it doesn’t directly generate pixel-level outputs; instead, it predicts in a higher-level representation space, allowing the model to focus on learning more semantic features. Another core idea of JEPA is to train the network through self-supervised learning so that it can predict missing or hidden parts in the input data. Through self-supervised learning, the model can pretrain on a large amount of unlabeled data, then fine-tune on downstream tasks, thus improving its performance on various visual and general tasks.

The Transformer originates from natural language processing tasks. Based on the principle of attention mechanism, it allows the model to pay attention to different parts of the input data at the same time. In many domains that require long-term dependencies and memory-based reasoning, the Transformer has been proven to be more effective than recurrent neural networks, and therefore has received increasing attention in the field of reinforcement learning in recent years. Since 2022, there have been several pieces of work attempting to build world models based on Transformer and its variants, achieving better performance on some complex memory interaction tasks than RSSM. Among them, Google’s Genie drew considerable attention. This work builds a generative interactive environment based on ST-Transformer, trained through self-supervised learning on large amounts of unlabeled internet video data. Genie demonstrates a new paradigm of customizable manipulative world models, offering massive potential for the future development of world models.

Lately, some methods have worked on building an intelligent agent world model based on diffusion models, with RoboDreamer learning constitutive world models to enhance the robot’s imagination. It decomposes the video generation process and utilizes the inherent combinability of natural language. In this way, it can synthesize videos of unseen combinations of objects and actions. RoboDreamer decomposes language instructions into a set of basic elements, then used as different conditions for a set of model-generated videos. This approach not only demonstrated powerful zero-sample generalization capabilities but also achieved impressive results in multimodal instruction video generation and robot operation task deployment. UniSim is a generative simulator for real-world physical interactions. UniSim includes a unified generative framework that takes action as input and integrates various datasets. Through this approach, UniSim can simulate the visual results of high-level instructions and low-level control, allowing for the creation of controllable game content and training embodied intelligent objects in a simulated environment.

Challenges and future directions of development
Although research into general world models and specific applications, such as autonomous driving and robots, has escalated in recent years, there are still many challenges and opportunities awaiting further exploration. This review also delves into the intricate challenges currently faced by general world models while envisaging potential directions for future development.
Challenge one: Causal and counterfactual reasoning
As a predictive model, the essence of world modeling is its ability to infer the future. The model should be able to infer the result of decisions it has never encountered before, rather than simply predict within known data distributions. As shown in the figure below, we expect world models to have counterfactual reasoning capabilities, inferring results through rational imagination. This ability is innately present in humans but remains a challenging task for current AI systems.

For instance, imagine an autonomous vehicle faced with a sudden traffic accident or a robot in a new environment. A world model with counterfactual reasoning capabilities can simulate the different actions they might take, predict outcomes, and choose the safest response. This would significantly improve the decision-making abilities of autonomous intelligent systems, helping them handle new and complex scenarios.

Is Sora a world simulator? The world's first comprehensive review analyzes the universal world model.
Challenge two: Simulating physical laws.
Although Sora’s video generation capabilities are impressive, many researchers believe it is premature to consider it as a world model because it doesn’t fully obey physical laws. The real world requires strict adherence to physical laws, such as gravity, light interaction, and fluid dynamics. While Sora has made improvements in modeling motion, including pedestrians and rigid body motion, it still performs poorly in accurately simulating fluids and complex physical phenomena. Training merely on video-text pairs is inadequate to understand these complexities – joint training with data produced by a physical renderer may be a potential solution.
Challenge three: Generalization ability.
Generalization ability is one of the keys to evaluate the performance of a world model, emphasizing not just data interpolation, but more importantly, data extrapolation. For instance, in autonomous driving, real traffic accidents or abnormal driving behaviors are rare events. So, can the learned world model imagine these rare driving events? This requires the model to go beyond simply memorizing training data and develop a profound understanding of driving principles. Through extrapolating from known data and simulating a variety of potential situations, the world model can navigate better safely in the real world.
Challenge four: Computational efficiency.
The efficiency of video generation is a key factor limiting its large-scale application. To maintain the consistency of video generation, the commonly used temporal consistency module can greatly increase the generation time. According to news and analysis on the internet, Sora may require about an hour to generate one minute of video. Although a series of distillation-based methods have emerged in the field of image generation, significantly speeding up the generation speed, the relevant research in the field of video generation is still very limited.
Challenge five: Performance evaluation.
The current research hotspots of world models mainly focus on generative models, and the evaluation indicators are mainly the quality of generation, such as FID and FVD, etc. In addition, some work has proposed more comprehensive evaluation benchmarks, such as CLIPScore, T2VScore, VBench, EvalCrafter, PEEKABOO, etc. However, a single measurement number cannot fully reflect the predictability of world models. Combining human feedback can make the evaluation more comprehensive, but how to improve its efficiency and consistency is a direction worth further research.
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