Sora and Beyond: Comprehensive Survey on Generative World Models for Video, Autonomous Driving, and Intelligent Agents

Keskeiset käsitteet

General world models represent a crucial pathway toward achieving Artificial General Intelligence (AGI), serving as the cornerstone for various applications ranging from virtual environments to decision-making systems. This survey provides a comprehensive exploration of the latest advancements in world models, including their applications in video generation, autonomous driving, and the development of autonomous agents.

Tiivistelmä

This survey presents a holistic examination of recent advancements in world model research, encompassing profound philosophical perspectives and detailed discussions. The analysis delves deeply into the literature surrounding world models for video generation, autonomous driving, and autonomous agents, uncovering their applications in media production, artistic expression, end-to-end driving, games, and robots. The survey also assesses the existing challenges and limitations of world models and explores prospective avenues for future research, with the intention of steering and igniting further progress in world models.

The survey first introduces the technologies behind video generation models, including visual foundation models, text encoders, and various generation techniques such as GAN, diffusion, autoregressive modeling, and masked modeling. It then reviews the advanced video generation models that have emerged in recent years, categorizing them into GAN-based, diffusion-based, autoregressive modeling-based, and masked modeling-based methods. The survey also discusses the Sora model, which is considered a significant breakthrough in video generation and a potential pathway towards world models.

Next, the survey delves into the applications of world models in autonomous driving. It presents two primary types of world models within autonomous driving: world models for end-to-end driving and world models as neural driving simulators. The survey examines methods such as Iso-Dream, MILE, SEM2, and TrafficBots, which leverage world models to enhance decision-making and future prediction capabilities in autonomous driving scenarios.

Finally, the survey explores the role of world models in the development of autonomous agents, highlighting their applications in game agents, robotic systems, and broader contexts. It discusses approaches like the Dreamer series, UniPi, UniSim, RoboDreamer, and LeCun's Joint-Embedding Predictive Architecture (JEPA), which demonstrate the versatility and potential of world models in enabling intelligent interactions across diverse environments.

The survey concludes by assessing the existing challenges and limitations of world models and discussing their potential future directions, aiming to inspire continued innovation and progress in this field.

Tilastot

"General world models seek to understand the world through generative processes."
"World models predict the future to grow comprehension of the world."
"The ability of world models to understand the environment not only enhances video generation quality, but also benefits real-world driving scenarios."
"World models have increasingly become integral to the functioning of autonomous agents, facilitating intelligent interactions across a myriad of contexts."

Lainaukset

"General world models represent a crucial pathway toward achieving Artificial General Intelligence (AGI), serving as the cornerstone for various applications ranging from virtual environments to decision-making systems."
"World models predict the future to grow comprehension of the world. This predictive capacity holds immense promise for video generation, autonomous driving, and the development of autonomous agents, which represent three mainstream directions of development in world models."
"The multifaceted applications of world models extend beyond games and robotics. LeCun's proposal of the Joint-Embedding Predictive Architecture (JEPA) heralds a significant departure from traditional generative models."

Tärkeimmät oivallukset

Is Sora a World Simulator? A Comprehensive Survey on General World Models and Beyond

by Zheng Zhu,Xi... klo arxiv.org 05-07-2024

https://arxiv.org/pdf/2405.03520.pdf

Syvällisempiä Kysymyksiä

How can world models be further integrated with physical simulation engines to enhance their understanding of the physical world?

World models can be enhanced by integrating them with physical simulation engines to deepen their comprehension of the physical world. By combining world models with simulation engines, we can create more realistic and dynamic environments for the models to interact with. This integration can lead to improved predictive capabilities and a better understanding of how different factors interact in the real world.
One way to integrate world models with simulation engines is to feed real-world data into the simulation to create more accurate and detailed virtual environments. This data can include information about physical properties, environmental conditions, and other relevant factors that impact the behavior of the system being modeled. By incorporating real-world data into the simulation, the world model can learn from more diverse and complex scenarios, leading to a more robust understanding of the physical world.
Furthermore, the integration of world models with physical simulation engines can enable the models to test hypotheses and scenarios in a controlled environment before applying them in the real world. This allows for experimentation and exploration of different possibilities without the risk of real-world consequences. By running simulations based on the world model's predictions, researchers can validate the model's accuracy and refine its understanding of the physical world.
In summary, integrating world models with physical simulation engines can enhance their understanding of the physical world by providing more realistic and dynamic environments for modeling and experimentation. This integration can lead to improved predictive capabilities, better decision-making, and a deeper insight into the complexities of the real world.

How can world models be leveraged to facilitate cross-modal learning and reasoning, enabling seamless integration of visual, linguistic, and other modalities?

World models can play a crucial role in facilitating cross-modal learning and reasoning by enabling the seamless integration of different modalities such as visual, linguistic, and other sensory inputs. By leveraging world models, researchers can create a unified framework that can process and understand information from multiple modalities simultaneously, leading to more comprehensive and nuanced insights.
One way to achieve cross-modal learning and reasoning is to train world models on diverse datasets that contain information from various modalities. By exposing the model to a wide range of inputs, including images, text, audio, and other sensory data, the model can learn to extract meaningful relationships and patterns across different modalities. This multi-modal training can help the model develop a holistic understanding of the world and how different modalities interact and influence each other.
Additionally, world models can be designed with architectures that support cross-modal reasoning, allowing them to infer relationships and make predictions based on inputs from different modalities. For example, models with attention mechanisms can focus on relevant information from different modalities and integrate them to generate coherent outputs. By enabling the model to reason across modalities, researchers can harness the power of multi-modal data to enhance decision-making, problem-solving, and understanding complex phenomena.
Furthermore, world models can be used to create multi-modal embeddings that represent information from different modalities in a shared space. These embeddings can capture the relationships between different modalities and enable seamless integration and comparison of diverse data types. By leveraging these embeddings, researchers can perform tasks such as image captioning, visual question answering, and cross-modal retrieval with greater accuracy and efficiency.
In conclusion, world models can be leveraged to facilitate cross-modal learning and reasoning by training on diverse datasets, designing architectures that support multi-modal processing, and creating multi-modal embeddings. This approach enables the seamless integration of visual, linguistic, and other modalities, leading to a more comprehensive understanding of the world and enhanced capabilities in multi-modal tasks.