在 计算机图形学、 三维视觉 、虚拟人、XR 领域 ，SIGGRAPH 是毫无争议的 "天花板级会议"。 SIGGRAPH Asia 作为 SIGGRAPH 系列两大主会之一，每年只接 收全球最顶尖研究团队的成果稿件，代表着学术与工业界的 最高研究水平与最前沿技术趋势 。 我们是淘宝技术 - Meta 技术团队，在 3D、XR、3D 真人数字人和三维重建等方向拥有深厚的技术积累和业务沉淀，我们自研了专业的多视角拍摄影棚，在今年 CVPR 2025 会议上作为 Highlight Paper 发表了 TaoAvatar ，并在淘宝未来旗舰店中实现了业内首个 3D 真人导购体验，下面视频展示了杭州西溪园区 C 区淘宝未来 旗舰店的精彩瞬间，欢迎大家到来访园区进行体验。 今年我们团队迎来另一个重要里程碑：我们撰写的针对移动端的高保真实时 3D 数字人重建与渲染系统论文 首次登录了国际顶级计算机图形学会议 SIGGRAPH Asia ！这是我们技术实力的一次正式 "官宣"，也是我们在 3D/XR 方向长期投入的阶段性成果展示。 我们研发的基于手机单目视频生成高保真且可实时驱动的 3D 数字人的系统名叫 HRM²Ava ...

Core Insights - The article highlights the significant achievement of Moore Threads at the SIGGRAPH Asia 2025, where the company won a silver medal in the 3D Gaussian Splatting Reconstruction Challenge, showcasing its advanced algorithm capabilities and hardware-software optimization in next-generation graphics rendering technology [1][17]. Group 1: 3D Gaussian Splatting Technology - 3D Gaussian Splatting (3DGS) is a revolutionary 3D scene representation and rendering technology introduced in 2023, achieving a remarkable balance between image quality, efficiency, and resource usage, with rendering efficiency improved by hundreds to thousands of times compared to traditional NeRF [4][8]. - The technology demonstrates strong adaptability and scalability in areas such as ray tracing, real-time VR/AR rendering, and multimodal fusion, making it a foundational technology for embodied AI, which requires high-quality, low-latency 3D environment modeling [7][8]. Group 2: Competition Details - The 3DGS Reconstruction Challenge required participants to complete high-quality 3DGS reconstruction within 60 seconds using real terminal video sequences and imperfect camera trajectories, emphasizing the challenge of achieving both reconstruction quality and speed [10][12]. - The evaluation metrics included PSNR (Peak Signal-to-Noise Ratio) for reconstruction quality and time taken, ensuring a fair and transparent ranking process [12][14]. Group 3: Moore Threads' Performance - Moore Threads' AI team, competing under the identifier "MT-AI," achieved a commendable balance in reconstruction accuracy and efficiency, securing the second place with an average PSNR of 27.58 and a reconstruction time of 34 seconds [17][21]. - The results from the competition indicated that Moore Threads' performance was competitive, with the top team achieving a PSNR of 28.43 and a reconstruction time of 57 seconds [18]. Group 4: LiteGS Library - Moore Threads developed the LiteGS library, which optimizes the entire pipeline from GPU systems to data management and algorithm design, achieving a PSNR of 27.58 and a reconstruction time of 34 seconds, significantly ahead of many competitors [21][24]. - LiteGS can achieve up to 10.8 times training acceleration while reducing parameter count by over 50%, demonstrating its engineering practicality and technological foresight [25][31]. - The library has been fully open-sourced on GitHub to promote collaborative development and continuous evolution in 3D reconstruction and rendering technology [27].

深圳大学黄惠团队独立推出 CLR-Wire：连续潜空间驱动的三维曲线框生成方法，首次实现了将复杂的三维曲线框结构统一编码到连续的潜空间中，解决了传统方 法难以同时有效捕捉线框几何和拓扑信息的难题。这一创新技术能够实现复杂三维结构的高效生成与平滑插值，在工业设计、三维重建及内容创作等领域具有广 泛的实际应用前景。第一作者为深圳大学可视计算研究中心 (VCC) 博士研究生马雪奇，合作者刘奕林、高天龙、黄期瑞均为 VCC 研究生。CLR-Wire 相关代码已 全面开源，欢迎大家试用和建议。 在计算机图形学的世界里，当我们谈论三维线框插补时，我们在讨论些什么？ 或许，是如何让一个圆柱平滑地演变为一个精致的碟状结构；或许，是如何巧妙地将一个醒酒器无缝过渡为圆润的花瓶；甚至，是如何从一栋带有屋顶的建筑 物，逐渐变化为简单明朗的方形结构，以及诸如漏斗或盘状结构之间的自由形态过渡。 该工作提出了 CLR-Wire，首先，通过多层交叉注意力将神经参数化曲线及其离散拓扑关系联合编码为定长潜向量，并借助变分自编码器构建连续的潜空间分布； 随后，采用流匹配方法实现从高斯噪声到完整线框的生成，并支持无条件生成以及基于点云、图像的条件生 ...