关于3D/4D 世界模型近期发展的总结和思考

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].