We are an inter-disciplinary team of researchers working in visual computing, in particular, computer graphics and computer vision. Current areas of focus include 3D and robotic vision, 3D printing and content creation, animation, AR/VR, geometric and image-based modelling, machine learning, natural phenomenon, and shape analysis. Our research works frequently appear in top venues such as SIGGRAPH, CVPR, and ICCV (we rank #11 in the world in terms of top publications in visual computing, as of 7/2020) and we collaborate widely with the industry and academia (e.g., Adobe Research, Google, MSRA, Princeton, Stanford, and Washington). Our faculty and students have won numerous honours and awards, including FRSC, Alain Fournier Best Thesis Award, Google Faculty Award, TR35@Singapore, NSERC Discovery Accelerator, and several best paper awards from ECCV, SCA, SGP, etc. Gruvi alumni went on to take up faculty positions in Canada, the US, and Asia, while others now work at companies including Apple, EA, Facebook, Google, IBM, and Microsoft.
October 10, 2020
Richard Zhang will deliver one of the keynote talks (virtually) at ChinaGraph 2020 held in Xiamen, China, on October 23. ChinaGraph is a bi-annual conference on computer graphics in China, the most important gathering of graphics researchers, students, and industries in the country. In another news, 3D-FRONT, a large-scale 3D indoor scene dataset published earlier this year by Richard and colleagues from Alibaba and the Chinese Academy of Sciences, has won the inaugural ChinaGraph Best Dataset Award.
September 25, 2020
Congratulations to Angel Xuan Chang, Yasutaka Furukawa and Manolis Savva for receiving fundings form Canada Foundation for Innovation (CFI). This funding will allow our researchers to take their transformative discoveries to the next level. More information about the funding and the projects can be found here here.
September 23, 2020
Title: Category-Level Object Perception for Physical InteractionTime: 1:30 - 2:30, Wednesday, September 23Abstract: Deep neural networks have shown great success both in semantic perception tasks, e.g. object recognition and semantic segmentation, and in end-to-end perception for reinforcement learning and robotic tasks. However, it is still unclear how to bridge these two perception paradigms to gain a deep semantic and interaction-driven understanding of physical interaction.In this talk, I will focus on how to explore categorical actionable information for the sake of perceiving and understanding physical interactions. First, I will show that learning high-level semantic actionable information, e.g. object state, can help with action planning. Second, I will introduce the problem of estimating category-level 6D pose and 3D size for rigid objects. This category pose can be seen as low-level actionable information and can benefit object manipulation tasks. Lastly, I will present my recent works on curating an articulated object dataset and estimating category-level articulated object pose.Bio: He Wang is a fifth-year PhD student at Stanford University under the supervision of Prof. Leonidas Guibas. His research interests span across computer vision, geometric computing, and robotics. In his PhD he contributed to generative modeling of human object interactions, opened up a new direction in estimating category-level pose and size for rigid and articulated objects. He receives Eurographics 2019 best paper honorable mention award and three of his works are accepted as CVPR oral presentations. Prior to his PhD he obtained his bachelor in Microelectronics from Tsinghua University.
September 13, 2020
Kevin (Kai) Xu, a former gruvier from 2008 to 2010, when he worked as a visiting Ph.D. student under the supervision of Richard Zhang, is now resuming his affiliation with GrUVi after being appointed as an Adjunct Professor at SFU CS for a three-year term. Welcome back to GrUVi, Kevin!Check his website for more information about his recent works in computer graphics and computer vision!