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].