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视频生成告别“瞬移变形”,群核科技Hugging Face登顶背后:空间语言改写AI物理世界规则

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]