Core Insights - AIGC technology is evolving from text and image generation to more complex 3D space and video domains, facing challenges in understanding physical world structures and maintaining temporal consistency in video creation [2][6] - Spatial intelligence is identified as a crucial bridge for AI to transition from the digital to the physical world, requiring AI to learn the "language" of space [2][9] Model Developments - The newly released models, SpatialLM 1.5 and SpatialGen, address the challenges of 3D scene generation and video creation, with SpatialLM 1.5 focusing on structured generation through "spatial language" and SpatialGen ensuring spatial coherence across multiple perspectives [3][4] - SpatialLM 1.5 encodes spatial relationships as "language," allowing for end-to-end generation of 3D scenes based on user input, producing structured scripts with physical parameters [4][5] Data and Training - The scarcity of high-quality 3D data is a significant bottleneck for spatial intelligence development, with over 4.41 billion 3D models and 500 million structured 3D scenes available by mid-2025 [5] - The company leverages its platform, CoolJia, to accumulate data that enhances the training of spatial understanding and generation models, creating a feedback loop between tools, data, and models [5] Video Generation Innovations - Current AI video generation tools struggle with spatial logic due to their reliance on 2D image sequences, leading to issues like object distortion and inconsistency [6][7] - SpatialGen overcomes these limitations by using a 3D Gaussian scene as an intermediary, allowing for the generation of images from any perspective while maintaining object consistency across frames [6][7] Market Strategy and Ecosystem - The company emphasizes open-sourcing its models and data to foster collaboration and innovation in the spatial intelligence market, aiming to expand the ecosystem rather than monopolize it [9][10] - The open-source strategy has garnered international attention, with the company releasing the world's first 3D Gaussian dataset, which has implications for various industries, including autonomous driving [9][10] Differentiation and Future Directions - The company's focus on interactive functional scenes differentiates it from other models that may lack spatial consistency, positioning it for industrial applications [10][11] - By providing a new path for industrial software development, the company aims to create "AI-native" design tools that bypass traditional complex geometric algorithms [11]

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].