Core Viewpoint - The article discusses the emergence of 3D content generation models, highlighting the unique approach of Qunhe Technology in developing a spatial large model that addresses the core industry pain point of "spatial consistency" [2][7]. Group 1: Current Landscape of 3D Content Generation - Major players in the 3D content generation space include Google Genie 3 and World Labs, focusing on either video generation or 3D scene generation [5]. - The "video generation faction," represented by Genie 3, can create dynamic interactive content but struggles with maintaining three-dimensional spatial consistency [5]. - The "3D scene generation faction," represented by World Labs and others, can achieve 360-degree roaming but often faces issues with scene collapse and content inconsistencies due to a lack of high-quality 3D data [5][11]. Group 2: Qunhe Technology's Spatial Large Model - Qunhe Technology's spatial large model aims to overcome the challenges faced by existing models, particularly in terms of spatial consistency and realistic roaming capabilities [8][12]. - The model is characterized by three features: realistic holographic roaming scenes, interactivity, and complex spatial processing capabilities [13]. - Qunhe has released two sub-models: SpatialLM 1.5 (spatial language model) and SpatialGen (spatial generation model), which exemplify these features [14]. Group 3: Spatial Language and Interaction - Spatial language, as defined by Qunhe, allows the model to describe 3D scenes in terms of spatial parameters, enhancing its ability to support precise spatial generation and editing [21]. - The model can assist robots in understanding complex spatial tasks by incorporating physical parameters and spatial knowledge [19][21]. - Compared to traditional models, SpatialLM 1.5 demonstrates superior performance in spatial understanding and task execution [30][32]. Group 4: Challenges and Industry Context - The spatial intelligence field is still in its early stages, akin to the GPT-2 phase, facing challenges such as data scarcity, high acquisition costs, and complex scene semantic understanding [32][51]. - Qunhe Technology's strategy involves a "three-in-one" approach, integrating spatial editing tools, spatial synthetic data, and spatial large models to create a positive feedback loop for development [42][45]. - The company has built the largest indoor space deep learning dataset, InteriorNet, with over 441 million 3D models and 500 million structured 3D space scenes, enhancing its competitive edge in the spatial intelligence domain [45]. Group 5: Future Prospects - The article emphasizes the potential for rapid growth in the spatial intelligence sector, driven by collaborative efforts and open-source initiatives [52]. - Qunhe Technology aims to accelerate the evolution of spatial intelligence and expand the industry by fostering a community of developers and researchers [54].