鱼羊 发自 凹非寺 量子位 | 公众号 QbitAI 华为在 世界模型 上又有新动作：投了一家物理AI公司。 极佳视界 ，最新完成一轮亿元级A1轮融资，由华为哈勃、华控基金联合投资。这也是该公司两个月内连续完成的第三轮融资。 这家公司成立于2023年，说得上是国内第一家"纯血"物理AI公司——创业就是为世界模型而来。 2年时间，极佳视界的产品已经覆盖从自动驾驶世界模型、具身基础模型到世界模型平台的全栈软硬件， 应用落地领域，正是华为正在持续押 注的方向：自动驾驶和具身智能 。 "世界模型是未来重要高质量数据来源" 极佳视界核心团队成员来自清华大学、中科院、中科大等知名院校。 最早"出圈"，是在2024年奇绩创坛的路演上：联合清华大学自动化系，发布了国内首个支持原生16秒超长时长的视频生成模型"视界一粟 YiSu"。 当时，极佳视界就强调了视频生成模型对物理世界的理解，并展示了其在自动驾驶和具身智能领域的应用潜力。 极佳视界的创始人兼CEO 黄冠 ，是清华大学自动化系AI方向博士。创立极佳视界之前，拥有微软、三星、地平线等公司的算法经历，以及 AI、自动驾驶等方向的连续创业经验。 在2024年的公开演讲中，黄冠 ...

Core Insights - Huawei, in collaboration with Shanghai Jiao Tong University and Huazhong University of Science and Technology, has launched a world model called WordGrow, capable of generating large indoor scenes of up to 1800 square meters in size [1][12]. Group 1: Technology Overview - WordGrow can generate a 272 square meter indoor scene in just 30 minutes using a single A100 GPU, achieving a speed six times faster than similar technologies [11]. - The model employs three core technologies: precise data preprocessing, a 3D block completion mechanism, and a coarse-to-fine generation strategy, which collectively enhance the quality and coherence of generated scenes [9][10]. Group 2: Performance Metrics - Experimental data indicates that WordGrow achieves state-of-the-art (SOTA) geometric reconstruction metrics, with a low Fréchet Inception Distance (FID) of 7.52, significantly outperforming mainstream methods like SynCity and BlockFusion [12]. - Even when expanded to a 7x7 block ultra-large scene, the edge quality remains stable, demonstrating the robustness of the model [10]. Group 3: Team and Research Background - The primary authors of the research are Sikuang Li and Chen Yang from Shanghai Jiao Tong University, who completed the study during their internship at Huawei. The research focuses on computer vision and computer graphics [13].

Core Viewpoint - The article discusses the launch of WordGrow, a world model developed by Huawei in collaboration with Shanghai Jiao Tong University and Huazhong University of Science and Technology, capable of generating large indoor scenes with high realism and coherent geometry [1][2]. Group 1: Technology Overview - WordGrow can generate an indoor scene of 1800 square meters (19x39 blocks) in just 30 minutes on a single A100 GPU, achieving a speed six times faster than similar technologies [16][17]. - The model employs three core technologies: precise data preprocessing, a 3D block completion mechanism, and a coarse-to-fine generation strategy [10][12][14]. - The model's geometric reconstruction metrics, MMD and COV, have reached state-of-the-art levels, with a FID score as low as 7.52, significantly outperforming mainstream methods like SynCity and BlockFusion [17]. Group 2: Technical Details - The first step involves data preprocessing from large datasets like 3D-FRONT, ensuring high-quality sample extraction and scene segmentation [10]. - The second step focuses on seamless integration of 3D structures, maintaining consistent visual styles and eliminating issues like texture misalignment [12]. - The final step enhances scene resolution and detail by refining the overall layout and filling in missing elements such as furniture and textures [12][14]. Group 3: Performance Metrics - Experimental results indicate that even when expanded to a 7x7 block ultra-large scene, the edge quality remains stable [15]. - The model's performance metrics show a significant improvement over competitors, with MMD values of 0.97 and EMD values indicating superior quality [15][16]. Group 4: Team Background - The research was conducted by Sikuang Li and Chen Yang from Shanghai Jiao Tong University during their internship at Huawei, with guidance from renowned AI expert Tian Qi [18][19]. - Tian Qi is recognized as the Chief Scientist of Huawei's Terminal BG and an esteemed member of international scientific communities [20].