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.
Sep 14th, 2022
Congratulations to Qimin Chen, Wallace Lira, and Sonia Raychaudhuri! They were selected as recipients for the inaugural CS Outstanding Teaching Assistent Award.
This award recognizes exceptional TAs in the School of Computing Science for their outstanding work in teaching and performing other TA duties. The recipients will be acknowledged at the October 20th school meeting.
Sep 14th, 2022
Congratulations to Qimin Chen, Wallace Lira, and Sonia Raychaudhuri! They were selected as recipients for the inaugural CS Outstanding Teaching Assistent Award. This award recognizes exceptional TAs in the School of Computing Science for their outstanding work in teaching and performing other TA duties. The recipients will be acknowledged at the October 20th school meeting.
Aug 7th, 2022
The first SFU Visual Computing Workshop was held on Aug 7th at the Morris J. Wosk Centre for Dialogue in Vancouver, Canada. This workshop preceded SIGGRAPH 2022, so it was conceived as a warmup for the researchers that were gathering for the most prestigious Computer Graphics conference.
The workshop was organized by Daniel Cohen-Or, Richard Zhang, Ali Mahdavi-Amiri, and Wallace Lira. The event featured some of the leading voices and rising stars in the Computer Graphics community, where the panelists included Angel Chang, Yasutaka Furukawa, Rana Hanocka, Niloy Mitra, and Ariel Shamir. Further, the featured speakers were Amit Bermano, Jason Peng, Rana Hanocka, Niloy Mitra, Ming Lin, Yifan Wang, and Andrea Tagliasacchi.
Aug 7th, 2022
The first SFU Visual Computing Workshop was held on Aug 7th at the Morris J. Wosk Centre for Dialogue in Vancouver, Canada. This workshop preceded SIGGRAPH 2022, so it was conceived as a warmup for the researchers that were gathering for the most prestigious Computer Graphics conference.The workshop was organized by Daniel Cohen-Or, Richard Zhang, Ali Mahdavi-Amiri, and Wallace Lira. The event featured some of the leading voices and rising stars in the Computer Graphics community, where the panelists included Angel Chang, Yasutaka Furukawa, Rana Hanocka, Niloy Mitra, and Ariel Shamir. Further, the featured speakers were Amit Bermano, Jason Peng, Rana Hanocka, Niloy Mitra, Ming Lin, Yifan Wang, and Andrea Tagliasacchi.
May 5th, 2022
Title: MyStyle: A Personalized Generative Prior
Time: Friday, May 6th at 11:00 AM PST
Abstract: Deep generative models have proved to be successful for many image-to-image applications. Such models hallucinate information based on their large and diverse training datasets. Therefore, when enhancing or editing a portrait image, the model produces a generic and plausible output, but often it isn’t the person who actually appears in the image. In this talk, I’ll present our latest work, MyStyle - which introduces the notion of a personalized generative model. Trained on ~100 images of the same individual, MyStyle learns a personalized prior, custom to their unique appearance. This prior is then leveraged to solve ill-posed image enhancement and editing tasks - such as super-resolution, inpainting and changing the head pose.
May 5th, 2022
Title: MyStyle: A Personalized Generative Prior Time: Friday, May 6th at 11:00 AM PST Abstract: Deep generative models have proved to be successful for many image-to-image applications. Such models hallucinate information based on their large and diverse training datasets. Therefore, when enhancing or editing a portrait image, the model produces a generic and plausible output, but often it isn’t the person who actually appears in the image. In this talk, I’ll present our latest work, MyStyle - which introduces the notion of a personalized generative model. Trained on ~100 images of the same individual, MyStyle learns a personalized prior, custom to their unique appearance. This prior is then leveraged to solve ill-posed image enhancement and editing tasks - such as super-resolution, inpainting and changing the head pose. MyStyle Paper Yotam Nitzan’s personal webpage
May 4th, 2022
Congratulations to Hao (Richard) Zhang and Manolis Savva for receiving awards at the Graphics Interface 2022. Richard has received the 2022 CHCCS/SCDHM Achievement Award of the Canadian Human-Computer Communications Society. Richard has had numerous high-impact contributions to computer graphics including geometric modeling, shape analysis, geometric deep learning, and computational design and fabrication. Manolis has received the 2022 Early Career Researcher Award. Manolis has established himself as a central figure in topics at the intersection of computer graphics, 3D sensing, and machine learning. To learn more about the research contributions of Richard and Manolis please checkout here and here.
May 4th, 2022
Congratulations to Hao (Richard) Zhang and Manolis Savva for receiving awards at the Graphics Interface 2022. Richard has received the 2022 CHCCS/SCDHM Achievement Award of the Canadian Human-Computer Communications Society. Richard has had numerous high-impact contributions to computer graphics including geometric modeling, shape analysis, geometric deep learning, and computational design and fabrication. Manolis has received the 2022 Early Career Researcher Award. Manolis has established himself as a central figure in topics at the intersection of computer graphics, 3D sensing, and machine learning. To learn more about the research contributions of Richard and Manolis please checkout here and here.
March 2nd, 2022
Congratulations to all Gruviers who are publishing their work at CVPR 2022. CVPR is the premier conference on computer vision and will be held in New Orleans this year. To learn more about the sample work that Gruvi will be presenting checkout here and here.
Dec 20th, 2021
We wrap-up the year 2021 with great achievements and look forward to the new year ahead of us. In 2021, Gruviers were able to publish their work at many 1st tier conferences: CVPR (12 papers), SIGGRAPH and SIGGRAPH Asia (4 papers), ICCV (4 papers), Eurographics and Neurips. Congratulations to all Gruviers for their hard work.
Oct 19, 2021
Title: SAPE: Spatially-Adaptive Progressive Encoding for Neural Optimization
Time: Wednesday, Nov 3, 1:30 PM
Abstract: Multilayer-perceptrons (MLP) are known to struggle with learning functions of high-frequencies, and in particular cases with wide frequency bands. We present a spatially adaptive progressive encoding (SAPE) scheme for input signals of MLP networks, which enables them to better fit a wide range of frequencies without sacrificing training stability or requiring any domain specific preprocessing. SAPE gradually unmasks signal components with increasing frequencies as a function of time and space. The progressive exposure of frequencies is monitored by a feedback loop throughout the neural optimization process, allowing changes to propagate at different rates among local spatial portions of the signal space. We demonstrate the advantage of SAPE on a variety of domains and applications, including regression of low dimensional signals and images, representation learning of occupancy networks, and a geometric task of mesh transfer between 3D shapes.
September 24th, 2021
Congratulations to Zhiqin Chen for receiving a PhD Fellowship from Google. The Google PhD Fellowship Program was created to recognize outstanding graduate students doing exceptional and innovative research in areas relevant to computer science and related fields. Fellowships support promising PhD candidates of all backgrounds who seek to influence the future of technology. To learn more about Zhiqin's research please visit here.
Aug 5, 2021
Title: Recent Advancements in StyleGAN Inversion
Time: Wednesday, August 11, 10AM
Abstract: StyleGAN has recently been established as the state-of-the-art unconditional generator, synthesizing images of phenomenal realism and fidelity. With its rich semantic space, many works have attempted to understand and control StyleGAN’s latent representations with the goal of performing image manipulations. To perform manipulations on real images, however, one must learn to “invert” the GAN and encode a given image into StyleGAN’s latent space, which remains an open challenge. In this talk, we will discuss recent techniques and advancements in GAN Inversion and explore their importance for real image editing applications. In addition, going beyond the inversion task, we demonstrate how StyleGAN can be used for performing a wide range of image-to-image translation tasks.
Bios: Or and Yuval are both graduate students studying Computer Science at Tel-Aviv University under the supervision of Professor Daniel Cohen-Or and have collaborated on numerous works in the past year. Their main interests lie in the field of Computer Vision with recent work centered around image generation and manipulation.
July 23rd, 2021
Congratulations to Nelson Nauata for receiving a Borealis AI 2021 Fellowship. Only ten students from the top academic institutions in Canada received fellowships this year, all with the goal of contributing to the advancement of artificial intelligence and machine learning. To learn more about the award and Nelson’s contributions check out here.
Jun 19, 2021
CVPR, the premier conference on computer vision, will be held virtually this year (June 19-25). GrUVi lab will once again have an incredible showing at CVPR, with 16 technical papers, 2 invited talks, 4 co-organized workshops, and 1 hosted challenge!
Workshop co-organization
GrUViers will co-organize 4 workshops featuring state-of-the-art research and will host one challenge:
Invited workshop talks
Yasutaka Furukawa will give a talk at the “Computer Vision in the Built Environment” workshop, while Manolis Savva will give a talk at the “3D Vision and Robotics” workshop.
Technical Papers and GrUVi authors
Congratulations to all authors for the accepted papers! The full list of papers featured on CVPR 2021 can be accessed here.
April 23, 2021
Talk title: Self–Adaptive Visual Learning
Time: April 23, from 3:30 to 4:30PM (PST)
Abstract: There have been significant advances in computer vision in the past few years. Despite the success, current computer vision systems are still hard to use or deploy in many real-world scenarios. In particular, current computer vision systems usually learn a generic model. But in real world applications, a single generic model is often not powerful enough to handle the diverse scenarios. In this talk, I will introduce some of our recent work on self-adaptive visual learning. Instead of learning and deploying one generic model, our goal is to learn a model that can effectively adapt itself to different environments during testing. I will present applications from several computer visions, such as crowd counting, anomaly detection, personalized highlight detection, etc.
Bio: Yang Wang is an associate professor in the Department of Computer Science, University of Manitoba. He is currently on leave and working as the Chief Scientist in Computer Vision, Noah‘s Ark Lab, Huawei Technologies Canada. He did his PhD from Simon Fraser University, MSc from University of Alberta, and BEng from Harbin Institute of Technology. Before joining UManitoba, he worked as a NSERC postdoc at the University of Illinois at Urbana-Champaign. His research focuses on computer vision and machine learning. He received the 2017 Falconer Emerging Researcher Rh Award in applied science at the University of Manitoba. He currently holds the inaugural Faculty of Science research chair in fundamental science at UManitoba.
