Core Insights - The article discusses the rapid advancements in AI 3D modeling tools, highlighting their potential to transform the design process from lengthy manual modeling to quick generation through simple inputs [2][3][37] - A comparative evaluation of several leading AI 3D modeling products is conducted to assess their performance in terms of generation quality, usability, and practical application potential [3][5][37] Group 1: AI 3D Modeling Tools Overview - Six representative AI 3D modeling tools were selected for evaluation: Hitems3D v1.5, 混元3D V3.0, Tripo, Hyper3D Gen-2, Meshy 6 Preview, and Genie (Luma AI) [5] - The tools come from various teams and ecosystems, ranging from large corporations to startups, illustrating the diverse landscape of the current AI 3D sector [5] Group 2: Evaluation Criteria and Methodology - The evaluation was conducted from the perspective of young, average users, focusing on practical usability and aesthetic appeal [4] - The assessment involved a series of tasks with increasing complexity to test the models' foundational generation capabilities and their stability in handling complex tasks [8][11] Group 3: Performance Results - All products demonstrated the ability to reconstruct the overall structure of the models, with 混元3D showing the most accurate facial details [12][18] - In terms of texture quality, Hitems3D and Tripo excelled in color and material fidelity, accurately reproducing details from reference images [13][22] - The models generally performed well in simpler tasks but struggled with complex details and facial features, often requiring significant manual post-processing to meet commercial standards [39][40] Group 4: Future Outlook - AI 3D modeling capabilities are advancing beyond mere novelty, showing potential for practical applications in design workflows, significantly reducing the time required for initial modeling tasks [37] - Despite impressive efficiency, there remains a gap in quality and detail handling, particularly in facial features and complex structures, indicating room for further development in the technology [38][39]

Group 1: Market Activity - Southbound funds net bought Hong Kong stocks worth 10.54 billion HKD on September 26, with notable purchases including Alibaba-W (2.412 billion HKD), Horizon Robotics-W (863 million HKD), Tencent Holdings (792 million HKD), and Xiaomi Group-W (605 million HKD) [1] - Southbound funds have continuously net bought Alibaba for 26 days, totaling 71.79789 billion HKD, and have net bought Tencent for 3 days, totaling 4.55499 billion HKD [3] Group 2: Company Updates - Alibaba announced at its annual shareholder meeting on September 25, 2025, that shareholders approved a general authorization for the board to issue up to 10% of the company's existing ordinary shares and to repurchase up to 10% of the company's ordinary shares [4] - Tencent's "Tencent Mixyuan" announced the release and open-sourcing of new 3D generative models, enhancing practical applications in gaming, printing, and AR/VR [4] - Xiaomi Group's chairman Lei Jun delivered his sixth annual speech, launching the Xiaomi 17 series and other new products, with the Xiaomi 17 starting at 4,499 RMB. Morgan Stanley expects strong sales performance for the Xiaomi 17 series, potentially accelerating Xiaomi's market share growth in the high-end smartphone segment [5] - Li Auto officially launched the Li Auto i6, priced at 249,800 RMB, featuring advanced specifications and a strong sales outlook due to a new vehicle cycle [6] - Xpeng Motors announced its entry into Switzerland, Austria, Hungary, Slovenia, and Croatia, with plans to launch new models in these markets in collaboration with Hedin Group and other partners [6]

Core Viewpoint - Tencent's Hunyuan3D-PolyGen introduces an advanced 3D generation model that significantly enhances the efficiency of 3D modeling, achieving over 70% improvement in productivity for artists in game development [2][19]. Group 1: Model Features and Performance - Hunyuan3D-PolyGen supports the generation of complex geometric models with thousands of polygons, transforming 3D models into assets [1][2]. - The model's topology function is now available on the Hunyuan3D platform, allowing for 20 free uses per day [3]. - The model distinguishes itself from standard 3D modeling by focusing on aspects such as polygon count, wireframe quality, and component structure, which are crucial for game rendering [4][19]. Group 2: Technical Implementation - Hunyuan3D-PolyGen utilizes a self-regressive mesh generation framework that processes vertices and faces for spatial reasoning [24]. - The model converts mesh structures into token sequences, which are then processed by a self-regressive model before being reconstructed into mesh format [27][30]. - The technology employs a high compression rate for mesh representation, reducing the number of tokens needed to represent a face from 9 to an average of 2.3, allowing for more complex models with over 20,000 polygons [36][37]. Group 3: Stability and Quality Improvements - The model incorporates a reinforcement learning framework to enhance generation stability, ensuring consistent quality across multiple outputs [40][43]. - The training framework uses artistic criteria such as wireframe neatness and geometric consistency as reward metrics to guide the model towards better results [41][43].