机器之心编辑部 刚刚，美国计算机协会 ACM（Association for Computing Machinery）公布了最新一届会士名单。 本年度新入选科学家中共有 71 人，他们的贡献涉及计算机图形学、网络安全、人机交互、数据管理、机器 学习、人工智能、算法、可视化等领域。 ACM 主席 Yannis Ioannidis 表示：这份入选名单代表了「我们领域当前正在发生的事情的快照。例如，今年 我们要表彰在计算机架构和软件工程等成熟学科工作的成员，以及在群体智能或场景识别等新兴学科的创 新者。」 机器之心对这些华人入选者进行了简单介绍（如有遗漏或错误，欢迎在留言区指正）。 Pei Cao ACM 创立于 1947 年，是全世界计算机领域影响力最大的专业学术组织之一。 ACM Fellow 是由该组织授予资深会员的荣誉，目的为表彰会员中对于计算机相关领域贡献前 1% 的学者， 其审查过程十分严格，每年遴选一次，研究员由同行提名，提名由委员会审查。 Pei Cao 是一位在业界极具影响力和知名度的技术专家与工程领导者。她曾先后在多家信息技术巨头任职， 包括谷歌和脸书，目前担任 YouTube 公司的工程副总裁 ...

Core Insights - The article discusses the academic contributions and achievements of Sida Peng, highlighting his research in computer vision and graphics, particularly in depth estimation and urban scene modeling [1][2][3]. Research Contributions - Sida Peng has authored multiple influential papers, including "InfiniDepth," "StreetCrafter," and "Street Gaussians," focusing on advanced techniques in depth estimation and urban scene synthesis [1][2]. - His work has led to significant advancements in video generation and dynamic urban modeling, utilizing precise laser radar data for improved accuracy [2]. Citation Metrics - Since 2021, Sida Peng's citation count has increased significantly, reaching 7,470 citations across 36 papers, with 58 papers cited at least 10 times [3][4]. - His h-index stands at 36, indicating a strong impact in his field, with notable papers such as "PVNet" and "Implicit Neural Representations" receiving high citation counts [4]. GitHub and Open Source Contributions - Sida Peng has a substantial following on GitHub, with 2.3K followers and numerous high-citation articles that have open-sourced their code, contributing to the academic community [5][6]. - His repositories include projects related to neural representations and real-time instance segmentation, showcasing his commitment to sharing knowledge and resources [5]. Academic Guidance and Mentorship - The article emphasizes the importance of mentorship in academic growth, detailing how Sida Peng's experiences under Professor Zhou Xiaowei have shaped his research capabilities and achievements [11]. - It highlights the supportive environment provided by mentors, which is crucial for developing research skills and achieving significant academic milestones [11].

Core Insights - The article discusses the advancements in 3D digital human reconstruction and rendering technology, specifically focusing on the HRM²Avatar system developed by the Taobao technology - Meta team, which allows for high-fidelity, real-time 3D digital humans to be created using only a smartphone [4][5][6]. Group 1: Technology Overview - HRM²Avatar is a system designed for high-fidelity real-time 3D digital human reconstruction and rendering, utilizing a two-stage capture method and a combination of explicit clothing mesh representation and Gaussian-based dynamic detail modeling [12][36]. - The system allows for the reconstruction of human figures, clothing structures, and detailed appearances under ordinary smartphone conditions, achieving a balance between visual realism, cross-pose consistency, and mobile real-time rendering [6][12]. Group 2: Methodology - The capture process involves both static and dynamic scanning phases, where users maintain a fixed pose for static scans and perform natural movements for dynamic scans, enabling the system to capture necessary signals for reconstruction and dynamic modeling [18][28]. - The system employs a mixed representation approach, attaching Gaussian points to the clothing mesh to provide controllable parameters for pose-related deformations and lighting modeling [40][46]. Group 3: Performance Evaluation - HRM²Avatar has been tested on mobile devices, achieving stable real-time performance with approximately 530,000 Gaussian points at 2K resolution and 120 FPS on the iPhone 15 Pro Max, and 2K at 90 FPS on Apple Vision Pro [87][89]. - Comparative evaluations show that HRM²Avatar outperforms existing methods in static reconstruction quality and appearance consistency under pose variations, as evidenced by higher PSNR and SSIM scores [76][80]. Group 4: Future Directions - The article emphasizes the ongoing need for optimization, particularly in handling complex clothing structures and extreme lighting conditions, indicating that HRM²Avatar is a significant milestone in making high-quality digital humans accessible to ordinary users [90].

Core Insights - The NVIDIA Graduate Fellowship Program has awarded scholarships to 10 doctoral students for the 2026 academic year, each receiving up to $60,000 to support their research in various fields related to computational innovation [2][4]. Group 1: Award Recipients - Jiageng Mao from the University of Southern California focuses on solving complex physical AI problems using large-scale internet data, aiming for robust and generalizable intelligence in real-world embodied agents [5]. - Liwen Wu from the University of California, San Diego specializes in computer graphics and 3D vision, with interests in neural rendering, inverse rendering, and 3D reconstruction [8]. - Sizhe Chen from the University of California, Berkeley is dedicated to ensuring AI safety in real-world applications, particularly developing defenses against prompt injection attacks [10]. - Yunfan Jiang from Stanford University is working on scalable methods for building general-purpose robots for everyday tasks using mixed data sources [12]. - Yijia Shao from Stanford University researches human-AI collaboration, developing AI agents that can communicate and coordinate with humans during task execution [14]. - Shangbin Feng from the University of Washington aims to advance model collaboration among machine learning models trained on different data [17]. - Irene Wang from Georgia Tech is developing a collaborative design framework for large-scale, energy-efficient AI training [19]. - Chen Geng from Stanford University focuses on modeling the 4D physical world using scalable data-driven algorithms [23]. - Shvetank Prakash from Harvard University is building AI agents using new algorithms and intelligent infrastructure [26]. - Manya Bansal from MIT is designing programming languages for modern accelerators to enable modular and reusable code without sacrificing performance [28]. Group 2: Finalists - The program also recognized five finalists: Zizheng Guo from Peking University, Peter Holderrieth from MIT, Xianghui Xie from the Max Planck Institute for Computer Science, Alexander Root from Stanford University, and Daniel Palenicek from Darmstadt University of Technology [31].

Core Insights - The article discusses the rising importance of 3DGS (3D Geometry Synthesis) technology in various fields, particularly in autonomous driving, healthcare, virtual reality, and gaming [2][4] - A comprehensive learning roadmap for 3DGS has been developed to address the industry's need for effective training in scene reconstruction and world modeling [4][6] Course Overview - The course titled "3DGS Theory and Algorithm Practical Tutorial" aims to provide a detailed understanding of 3DGS algorithms, covering both theoretical foundations and practical applications [6][10] - The course is designed in six chapters, starting from basic knowledge to advanced research directions in 3DGS [10][11] Chapter Summaries - **Chapter 1: Background Knowledge** Introduces foundational concepts in computer graphics, including implicit and explicit representations of 3D space, rendering pipelines, and tools like SuperSplat and COLMAP [10][11] - **Chapter 2: Principles and Algorithms** Focuses on the core principles of 3DGS, including dynamic and surface reconstruction, and introduces the 3DGRUT framework for practical learning [11][12] - **Chapter 3: 3DGS in Autonomous Driving** Highlights key works in the field, such as Street Gaussian and OmniRe, and utilizes DriveStudio for practical applications [12][13] - **Chapter 4: Important Research Directions** Discusses significant research areas like COLMAP extensions and depth estimation, emphasizing their relevance to both industry and academia [13][14] - **Chapter 5: Feed-Forward 3DGS** Explores the development and principles of feed-forward 3DGS, including recent algorithms like AnySplat and WorldSplat [14][15] - **Chapter 6: Q&A Discussion** Provides a platform for participants to discuss industry pain points and job demands related to 3DGS [15] Target Audience and Learning Outcomes - The course is aimed at individuals with a background in computer graphics, visual reconstruction, and programming, particularly those interested in pursuing careers in the 3DGS field [19][17] - Participants will gain comprehensive knowledge of 3DGS theory, algorithm development frameworks, and opportunities for networking with industry professionals [19][17]

Core Viewpoint - The research team led by Professor Huang Hui from Shenzhen University has developed a new generation of intelligent monitoring system for geological disasters, which integrates computer vision, deep learning, and cloud-edge-end collaborative technology, transforming traditional point-based monitoring into comprehensive and intelligent monitoring [1][3]. Group 1: Traditional Monitoring Limitations - Traditional geological disaster monitoring methods rely heavily on embedded sensors and manual inspections, which have significant limitations [3]. - Sensors can only monitor preset points and cannot cover entire risk areas, while manual inspections are constrained by weather and terrain, making many dangerous areas inaccessible [3]. Group 2: Technological Innovations - The team proposed a core graphic information "cloud-edge-end" collaborative processing technology, achieving a transition from point monitoring to comprehensive prevention [3]. - The system utilizes a combination of computer graphics, computer vision, and deep learning, with breakthroughs in three key technical areas: effective capture of abnormal movements in monitored areas, over 85% accuracy in identifying rockfall events, and high-precision measurement of target displacement [3]. Group 3: Application and Impact - The system has demonstrated its application value in various scenarios, including 24-hour monitoring of tunnel entrances and high slope sections on mountain roads, rockfall disaster warnings for railways, stability monitoring in open-pit mining, and ensuring the safety of slopes in water conservancy projects [5]. - It has been implemented in Shenzhen's Jiangangshan Park, providing continuous monitoring and alarm for dangerous rocks and rockfalls [5]. - The monitoring device is equipped with a large-capacity solar power system for uninterrupted operation, showcasing strong environmental adaptability and energy self-sufficiency [5]. - The system captures minute changes in rock formations using high-resolution cameras and analyzes data in real-time with built-in intelligent algorithms, triggering multi-level alerts and uploading data to a cloud management platform via 4G/5G networks [5]. - This technology marks a shift from passive waiting to proactive prediction in geological disaster monitoring and early warning, entering a new phase of "full-domain perception, intelligent deduction, and precise warning" [5].

Core Viewpoint - The article discusses the development of Sketch3DVE, a novel method for 3D scene video editing that allows users to manipulate videos using simple sketches, enhancing creativity and personalization in video content creation [3][22]. Part 1: Background - Recent advancements in video generation models have significantly improved text-to-video and image-to-video generation, with a focus on precise control over camera trajectories due to its important application prospects [6]. - Existing methods for video editing are categorized into two types: one directly uses camera parameters as model inputs, while the other constructs explicit 3D representations from single images to render new perspective images [8][9]. - Despite these advancements, editing real videos with significant camera motion remains a challenge, as video editing requires maintaining original motion patterns and local features while synthesizing new content [8][9]. Part 2: Algorithm Principles - Users begin by selecting the first frame of a 3D scene video, marking the editing area with a mask and drawing a sketch to specify the geometry of new objects [12]. - The system employs the MagicQuill image editing algorithm to process the first frame, generating the edited result, and utilizes the DUSt3R algorithm for 3D reconstruction to analyze the entire input video [13]. - A 3D mask propagation algorithm is designed to accurately transfer the mask from the first frame to subsequent frames, ensuring consistency across different perspectives [14]. - The final video generation model integrates edited images, multi-view videos, and original input videos to produce a scene-edited video with precise 3D consistency [14]. Part 3: Effect Demonstration - The method allows users to create high-quality 3D scene video edits, enabling operations such as adding, removing, and replacing objects while maintaining good 3D consistency [16]. - The approach can handle complex scenarios involving shadows and reflections, producing reasonable editing results due to training on real video datasets [17]. - Users can also edit the first frame using image completion methods, demonstrating the versatility of the system in generating realistic 3D scene video edits [19]. - Sketch3DVE offers an effective solution to traditional model insertion challenges, allowing for personalized 3D object generation and high-fidelity scene video editing without requiring extensive expertise [22].

Core Insights - The article emphasizes the rapid transformation of the world through artificial intelligence and programming technologies, highlighting the significance of Auckland University's computer science undergraduate program as a platform for students passionate about these fields [1]. Group 1: Program Advantages - Auckland University's computer science program boasts exceptional academic resources and a strong faculty, with the department recognized internationally for its research in artificial intelligence, data science, and cybersecurity [3]. - The faculty comprises professors from around the globe who have made significant academic contributions and maintain close collaborations with major tech companies like Google and Microsoft, integrating the latest industry trends into the curriculum [3]. - The university provides advanced learning resources, including high-performance computing clusters and virtual reality equipment, facilitating complex programming experiments and AI project development [3]. - Partnerships with numerous tech companies offer students internship and employment opportunities, allowing them to engage with real-world business projects during their studies [3]. Group 2: Application Requirements - Applicants to the computer science undergraduate program must meet specific academic and language criteria, with international students typically required to achieve an average high school score of over 80%, particularly excelling in mathematics and physics [4]. - For Chinese students, the Gaokao score is a critical reference, generally requiring a score above the provincial first-tier line; alternative qualifications like A-Level or IB scores are also accepted [4]. - Language proficiency is essential, with a minimum IELTS score of 6.5 (no individual score below 6.0) or a TOEFL score of 90 (with writing no less than 21) required for admission [4]. Group 3: Curriculum Content - The curriculum is diverse and designed to build a solid theoretical foundation and practical innovation skills, starting with introductory courses in computer science, programming basics (Python and Java), and discrete mathematics in the first year [6]. - As students progress, they encounter more specialized courses such as data structures and algorithms, computer systems principles, and database systems, deepening their understanding of computer science fundamentals [6]. - Elective courses in artificial intelligence, machine learning, computer graphics, and cybersecurity allow students to explore cutting-edge areas of interest, while project-based courses enable teamwork and problem-solving through real programming projects [6].