亚洲图形学学会日前在官网发布公告，宣布学会2026年度两个奖项的获奖者。中国图像图形学会常务理事、北京大学陈宝权教授获得杰出技术贡献奖(Outstanding Technical Contributions Award)，学会会员、香港科技大学的刘缘助理教授获得青年学者奖(Young Researcher Award)。

杰出技术贡献奖授予在计算机图形学领域取得杰出技术成就的个人。此前的获奖者包括郭百宁(2018)、鲍虎军(2019)、Takeo Igarashi(2020)、王文平(2021)、周昆(2022)、Ariel Shamir(2023)、童欣(2024)、黄惠(2025)。

陈宝权 北京大学 教授

亚洲图形学学会的官网评价陈宝权教授的学术成就如下：

Prof. Baoquan Chen is a distinguished researcher in computer graphics, 3D vision, and visualization, recognized for breakthrough contributions spanning fundamental algorithms, applied systems, and widespread industrial deployments.

In large-scale urban 3D reconstruction, Prof. Chen pioneered a knowledge-driven "top-down modeling" paradigm for mobile laser scanning, introduced robust $L_1$-medial skeleton extraction, and created PointCNN—the pioneering deep convolutional network for point clouds that remains a widely adopted backbone in the field. More recently, he proposed SLAM3R, an end-to-end feedforward network for real-time 3D reconstruction from monocular RGB videos. His technologies power major commercial platforms such as ESRI and Amap Global, generating 3D maps that cover millions of kilometers of urban roads, thereby laying critical data infrastructure for smart cities and embodied AI.

In physics-based simulation, Prof. Chen made significant advancements in simulating fluids, solids, and their complex mixtures, markedly improving both accuracy and efficiency. His simulations were highlighted by Nature Physics as being virtually indistinguishable from reality. Addressing multiphysics dynamics involving ferrofluids, viscoelastic media, thin shells, and their mutual coupling, he realized lightweight and efficient forward simulation along with inverse optimization solvers. Serving as foundational infrastructure, these tools enabled the design and fabrication of novel micro-magnetic robots—work published in a Nature portfolio journal. Furthermore, he established a 3D Gaussian Splatting (3DGS) based simulation framework that enables photorealistic, physically grounded generation of rainfall and flame phenomena in real-world environments.

In advanced manufacturing, Prof. Chen developed a comprehensive suite of methods for 3D printing shape design and path planning. By establishing a holistic approach to internal microstructure optimization under external geometry and physical performance constraints, he helped unify CAD, CAE, and CAM into an integrated paradigm. These innovations have been deployed in high-profile applications, including high-speed train windshields and aerospace heat exchangers.

Prof. Chen continuously pushes the boundaries of real-world application. A standout highlight is his work in sports broadcasting, where he pioneered a free-viewpoint system that leverages real-time rendering to compress novel view synthesis and replay generation from minutes down to mere seconds. Deployed extensively during the Beijing Winter Olympics, the system was praised by the International Olympic Committee for setting a new standard for Olympic broadcasting.

Prof. Chen publishes in premier venues such as SIGGRAPH, IEEE Visualization, ACM TOG, and IEEE TVCG. He is the recipient of the 2025 ACM SIGGRAPH Test-of-Time Award and multiple Best Paper honors. He served as Conference Chair for IEEE VIS 2005 and SIGGRAPH Asia 2014, and founded the China3DV conference. A Fellow of ACM, IEEE, CCF, and CSIG, he is also an inductee into both the ACM SIGGRAPH Academy and the IEEE Visualization Academy.

青年学者奖旨在表彰在职业生涯早期(获博士学位后不超过6年)，在计算机图形学领域做出显著贡献的亚洲青年学者。此前的获奖者包括Nobuyuki Umetani(2018)、胡瑞珍(2019)、高林(2020)、Yuki Koyama (2021)、Yifan "Evan" Peng (2022)、王鹏帅(2023)、Minhyuk Sung(2024)、Seung-Hwan Baek(2025)。

刘缘 香港科技大学 助理教授

亚洲图形学学会的官网评价刘缘教授的学术成就如下：

Yuan Liu is an Assistant Professor in the Division of Integrative Systems and Design at The Hong Kong University of Science and Technology (HKUST), where he leads the Intelligent Graphics Lab. Prior to joining HKUST, he was a postdoctoral researcher at Nanyang Technological University. He received his Ph.D. in Computer Science from The University of Hong Kong and obtained his master's and bachelor's degrees from Wuhan University.

Yuan’s research lies at the intersection of computer graphics, computer vision, and generative AI, with a focus on reconstructing, generating, and modeling 3D content from images and videos. His research contributions span three closely related directions. In neural rendering and surface reconstruction, his work on NeuS, NeuRay, and NeRO advanced neural implicit surface reconstruction, occlusion-aware image-based rendering, and the joint recovery of geometry and material appearance for challenging reflective objects. In 3D generation, his work on SyncDreamer, Wonder3D, Era3D, and DreamMat developed multiview-consistent diffusion models for single-image 3D generation and physically based material synthesis. More recently, his work on Align3R, TrackingWorld, and Track4World has explored temporally consistent depth estimation and dense, world-centric 3D reconstruction and tracking from monocular videos, while Diffusion as Shader introduced a 3D-aware video diffusion framework for versatile and controllable video generation. These works, published at leading venues including NeurIPS, CVPR, ICLR, SIGGRAPH, and ECCV, form a coherent line of research toward AI systems capable of reconstructing, generating, and modeling both static and dynamic 3D worlds.

Yuan also contributes actively to the research community. He serves as an Associate Editor for IEEE Transactions on Visualization and Computer Graphics and has served as an Area Chair for 3DV 2026, NeurIPS 2026 and a Technical Papers Committee member for SIGGRAPH Asia 2026.