阿里巴巴：阿里在港交所公告，在2025年9月25日召开的年度股东大会上，必要多数股东就以下议案投 赞成票：授予董事会在发行期内发行、配发及以其他方式处理不超过截至本普通决议通过当日公司已发 行普通股数量10%的公司新增普通股之一般授权，根据本授权发行及配发的任何普通股折价不得超过基 准价的10%。授予董事会在购回期内购回不超过截至本普通决议通过当日公司已发行普通股数量10%的 公司普通股之一般授权。 腾讯控股：据"腾讯混元"公众号消息，混元3D生成模型家族迎新——混元3D-Omni、混元3D-Part发布 并开源。这是腾讯混元在可控3D生成上的新突破，让AI 3D建模更具实用性，加速3D生成模型在游戏、 打印和AR/VR 等实际生产流程中的落地应用。 9月26日，南下资金今日净买入港股105.4亿港元。 其中，净买入阿里巴巴-W 24.12亿、地平线机器人-W 8.63亿、腾讯控股7.92亿、小米集团-W 6.05亿、 晶泰控股1.9亿、山高控股1.3亿、理想汽车-W 1.14亿；净卖出小鹏汽车-W 2.91亿、华虹半导体2.12亿、 中芯国际1.45亿。 据统计，南下资金已连续26日净买入阿里巴巴，共计71 ...

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].