Core Viewpoint - The article discusses the current state and future directions of embodied intelligence, particularly focusing on the development and optimization of 3D/4D world models, emphasizing the importance of data collection and utilization in training effective models [3][4]. Group 1: Current Research Focus - The majority of work in the first three quarters of the year has centered on data collection and utilization, specifically how to efficiently use video example data to train robust foundational models [3]. - There is a growing concern regarding the clarity and reliability of data collection methods, prompting a reevaluation of the approaches to data analysis and the development of 3D/4D world models [3][4]. Group 2: Approaches to 3D/4D World Models - Two main research approaches have emerged in the development of 3D/4D world models: implicit and explicit methods, each revealing limitations that have yet to be effectively addressed [4][7]. - Current research on explicit world models remains focused on static 3D scenes, with methods for constructing and enriching these scenes being well-established and ready for practical application [5]. Group 3: Challenges and Limitations - The existing methods for 3D geometry modeling, such as 3DGS, face challenges in surface optimization, leading to rough results despite attempts to improve through structured modifications [8]. - Issues related to lighting and surface quality in 3D reconstruction are being gradually optimized, but the overall design still faces significant hurdles, particularly in cross-physics simulator deployment [9]. Group 4: Future Directions - The article anticipates that future work will increasingly integrate physical knowledge into 3D/4D models, aiming to enhance the direct physical understanding and reasoning capabilities of models [15]. - There is an expectation for the emergence of new research that combines simulation and video generation to address existing gaps in the understanding of physical interactions and motion [14][15].

Core Viewpoint - The article discusses the current state of embodied intelligence, focusing on data collection and utilization, and emphasizes the importance of 3D/4D world models in enhancing spatial understanding and interaction capabilities in autonomous driving and related fields [3][4]. Group 1: 3D/4D World Models - The development of 3D/4D world models has diverged into two main approaches: implicit and explicit models, each with its own limitations [4][7]. - Implicit models enhance spatial understanding by extracting 3D/4D content, while explicit models require detailed structural information to ensure system stability and usability [7][8]. - Current research primarily focuses on static 3D scenes, with methods for constructing and enriching environments being well-established and ready for practical application [8]. Group 2: Challenges and Solutions - Existing challenges in 3D geometry modeling include the rough optimization of physical surfaces and the visual gap between generated meshes and real-world applications [9][10]. - The integration of mesh supervision and structured processing is being explored to improve surface quality in 3D reconstruction [10]. - The need for cross-physics simulator platform deployment is highlighted, as existing solutions often rely on specific physics parameters from platforms like Mujoco [10]. Group 3: Video Generation and Motion Understanding - The emergence of large-scale data cleaning and annotation has improved motion prediction capabilities in 3D models, with advancements in 3DGS/4DGS and world model integration [11]. - Current video generation techniques struggle with understanding physical interactions and changes in the environment, indicating a gap in the ability to simulate realistic motion [15]. - Future developments may focus on combining simulation and video generation to enhance the understanding of physical properties and interactions [15]. Group 4: Future Directions - The article predicts that future work will increasingly incorporate physical knowledge into 3D/4D models, aiming for better direct physical understanding and visual reasoning capabilities [16]. - The evolution of world models is expected to become modular within embodied intelligence frameworks, depending on ongoing research and simplification of world model definitions [16].

Core Viewpoint - The article discusses the current state and future directions of embodied intelligence, focusing on the collection and utilization of data for training effective models, particularly in the context of 3D/4D world models and their implications for autonomous driving and robotics [3][4]. Group 1: 3D/4D World Models - The development of 3D/4D world models has diverged into two main approaches: implicit and explicit models, each with its own limitations [4][7]. - Implicit 3D models enhance spatial understanding by extracting 3D/4D content, while explicit models require detailed structural information to ensure system stability and usability [7][8]. - Current research primarily focuses on static 3D scenes, with methods for constructing and enriching these environments being well-established and ready for practical application [8]. Group 2: Challenges and Solutions - Existing challenges in 3D geometry modeling include the rough optimization of physical surfaces and the visual gap between generated meshes and real-world applications [9][10]. - The integration of high-quality 3D reconstruction techniques is expected to address issues related to visual gaps and stability in geometry [10]. - Cross-platform deployment of physical simulators remains a challenge, with efforts like Roboverse aiming to create unified platforms to optimize physical expressions in world models [10]. Group 3: Video Generation and Motion Learning - The emergence of large-scale models has improved motion prediction capabilities, leading to advancements in the integration of 3D/4D models with video data [11][12]. - Current video generation techniques struggle with accurately simulating physical interactions and understanding the underlying physics of motion, which limits their effectiveness in real-world applications [15]. - Future developments may focus on combining simulation and video generation to enhance the understanding of physical properties and interactions [15]. Group 4: Future Directions - The article predicts that future work will increasingly incorporate physical knowledge into 3D/4D models, moving beyond mere geometric consistency to enhance predictive capabilities [16]. - The evolution of world models is expected to contribute to the development of embodied AI, emphasizing the need for improved physical understanding and reasoning abilities [16].