港中深韩晓光：3DGen，人类安全感之战丨GAIR 2025

Core Viewpoint - The article discusses the importance of understanding the underlying principles of world models, emphasizing that relying solely on data-driven approaches ("炼丹") is insufficient for creating effective AI systems. It advocates for the integration of human-understandable structures and logic into AI models to enhance their interpretability and reliability [2][63]. Group 1: Development of 3D Generation - The evolution of 3D generation has transitioned from early attempts at creating 3D models from single images to the current era of large models capable of generating high-quality 3D content from textual descriptions [7][16]. - The emergence of "open world" 3D generation began around 2023 with the Dreamfusion project, which allowed for the generation of 3D models without category restrictions, marking a significant shift in the field [11][12]. - Current trends in 3D generation focus on achieving finer details, structured outputs for easier editing, and better alignment between generated models and input images [19][20]. Group 2: Challenges and Opportunities in 3D Generation - The article highlights a dilemma faced by the 3D generation field, particularly in light of advancements in video generation technologies that can produce content without the complex 3D modeling processes [24][28]. - Despite the rise of video generation, 3D content creation retains its value due to its ability to provide physical realism, spatial consistency, and detailed control over content [29][34]. - The potential crisis for 3D generation lies in the increasing capabilities of video generation models, which are beginning to exhibit controllable features, raising questions about the necessity of 3D in future content creation [34][38]. Group 3: The Role of 3D in World Models - The article categorizes world models into three types: macro models for societal understanding, personal experience models for exploration, and embodied models for machine intelligence, with 3D being essential for interactive virtual environments [43][44][45]. - For embodied intelligence, understanding human interaction with the physical world necessitates 3D modeling to accurately capture and simulate these interactions [48][50]. - The transition from digital to physical manufacturing processes, such as 3D printing, underscores the foundational role of 3D data in creating tangible products [52]. Group 4: Technical Approaches in AI - The article contrasts explicit and implicit approaches in AI development, with explicit methods relying on clear geometric and physical modeling, while implicit methods depend on data-driven neural networks [56][57]. - The need for explainability in AI systems is emphasized, suggesting that a balance between performance and interpretability is crucial for user trust and safety [58][63]. - The discussion concludes that 3D and 4D modeling are vital for providing a comprehensible framework for understanding complex AI systems, thereby enhancing user confidence [59][63].