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.
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by Zheng Zhu,Xi... at arxiv.org 05-07-2024
https://arxiv.org/pdf/2405.03520.pdfDeeper Inquiries