September 29, 2024

The European Conference on Computer Vision (ECCV), a biennial premier research conference in Computer Vision and Machine Learning, will be held from September 29, 2024 to October 4, 2024 in Mico Milano, Italy. In addition to all accepted papers, GrUVi lab will also stand out in organizing multiple workshops.

Here are the information of workshops organized by our amazing professors:

• https://sites.google.com/view/cv4metaverse-2024/ - Organizers (Richard Zhang)
• https://3dv-in-ecommerce.github.io/ - Organizers (Richard Zhang, Angel Chang)
• https://ai3dcc.github.io/ - Organizers (Angel Chang, Manolis Savva)
• https://geo-lme.github.io/ - Speaker (Richard Zhang)

September 29, 2024

The European Conference on Computer Vision (ECCV), a biennial premier research conference in Computer Vision and Machine Learning, will be held from September 29, 2024 to October 4, 2024 in Mico Milano, Italy. GrUVi lab will once again have a good show at ECCV, with 9 technical papers.

Congratulations to all the authors!

And here are the 9 accepted papers:

• R3DS: Reality-linked 3D Scenes for Panoramic Scene Understanding
• DECOLLAGE: 3D Detailization by Controllable,Localized, and Learned Geometry Enhancement
• Generating Human Interaction Motions in Scenes with Text Control
• TC4D: Trajectory-Conditioned Text-to-4D Generation
• Lagrangian Hashing for Compressed Neural Field Representations
• Volumetric Rendering with Baked Quadrature Fields
• PointNeRF++: A multi-scale, point-based Neural Radiance Field
• SweepNet: Unsupervised Learning Shape Abstraction via Neural Sweepers
• Intrinsic Single-Image HDR Reconstruction

July 28, 2024

SIGGRAPH, the premier conference on computer graphics, will be held from July 28, 2024 to August 1, 2024 in Denver, United States. GrUVi lab will once again have a good show at SIGGRAPG, with 6 technical papers. Congratulations to all the authors!

And here are the 6 accepted papers:

• Scale-Invariant Monocular Depth Estimation via SSI Depth
• DAE-Net: Deforming Auto-Encoder for fine-grained shape co-segmentation
• Flexible Motion In-betweening with Diffusion Models
• SuperPADL: Scaling Language-Directed Physics-Based Control with Progressive Supervised Distillation
• Interactive Character Control with Auto-Regressive Motion Diffusion Models
• BrepGen: A B-rep Generative Diffusion Model with Structured Latent Geometry

June 17, 2024

The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR), the premier conference on computer vision, will be held in-person in Seattle, washington on June 17-21, 2024. GrUVi lab will once again have a good show at CVPR 2024, with 4 keynotes, 2 oral papers, 4 spotlights and 10 posters.

For more details, please refer to SFU @ CVPR 2024 (Technical Participation)

Congratulations!

June 13, 2024

Congratulations to Jason Peng for receiving the CHCCS/SCDHM Graphics Interface Early Career Researcher Award.

The CHCCS/SCDHM, Graphics Interface Early Career Researcher Award, aims to recognize, support and encourage outstanding early career faculty members in the fields related to the Graphics Interface conference, which covers all aspects of graphics, human-computer interaction, and visualization. The award is given annually to up to two individuals and presented at the annual Graphics Interface conference.

April 22, 2024

Congratulations to Zhiqin Chen for receiving both the 2024 Alain Fournier Award for the best Ph.D. dissertation in computer graphics in Canada and the 2024 Eurographics PhD Award for Best PhD Thesis.

The Alain Fournier Dissertation Award is given annually for an outstanding doctoral dissertation completed at a Canadian university in the field of Computer Graphics. The award is named in honor of Alain Fournier, a Canadian researcher who did much to promote excellence, both within Canada and internationally, in the field of Computer Graphics.

Eurographics PhD Award aims to recognize good thesis work, to incentivize young researchers, and to offer them the opportunity to publish the state of the art section of their thesis as a STAR in the Computer Graphics Forum Journal. Eurographics annually grants three PhD thesis awards. They are jointly sponsored by Eurographics and the Computer Graphics Forum Journal.

April 22, 2024

Eurographics, the premier conference on computer graphics, will be held in Limassol, Cyprus this year (April 22-26, 2024). GrUVi lab will once again have a good show at Eurographics 2024, with:

• Han-Hung Lee (along with Manolis Savva and Angel X. Chang) presenting Text-to-3D Shape Generation STAR.
• Zhiqin Chen receiving 2024 Eurographics PhD Award for Best PhD Thesis.
• Ali Mahdavi-Amiri serving as the International Program Committee member.

Congratulations!

March 18, 2024

3D Vision (3DV), the premier conference on computer vision, will be held in-person in Davos, Switzerland on March 18-21, 2024. GrUVi lab will once again have a good show at 3DV 2024, with 2 technical papers. And Andrea Tagliasacchi will be serving as the program co-chair for the conference.

Congratulations to all the authors! And here are the 2 accepted papers:

Generalizing Single-View 3D Shape Retrieval to Occlusions and Unseen Objects

OPDMulti: Openable Part Detection for Multiple Objects

February 23, 2024

VCR seminar will hold a workshop for AAAI visitors from other universities. Below is the schedule for the talks.

• 11 am-11:40 am Levi Lelis (U of Alberta)
• 11:40 am-12:20 pm Vahid Babaei (MPI) Inverse Design with Neural Surrogate Models
• 12:20 pm -1:00 pm lunch break
• 1 pm -1:40 pm Sven Koenig (USC) Multi-Agent Path Finding and Its Applications
• 1:40 pm -2:20 pm Jiaoyang Li (CMU) Layout Design for Large-Scale Multi-Robot Coordination

Talk 1 (11 am-11:40) Levi Lelis, Department of Computing Science, University of Alberta

Title: Learning Options by Extracting Programs from Neural Networks

Abstract: In this talk, I argue for a programmatic mindset in reinforcement learning, proposing that agents should generate libraries of programs encoding reusable behaviors. When faced with a new task, the agent learns how to combine existing programs and generate new ones. This approach can be helpful even when policies are encoded in seemingly non-decomposable representations like neural networks. I will show that neural networks with piecewise linear activation functions can be mapped to a program with if-then-else structures. Such a program can then be easily decomposed into sub-programs with the same input type of the original network. In the case of networks encoding policies, each sub-program can be seen as an option---a temporally extended action. All these sub-programs form a library of agent behaviors that can be reused later, in downstream tasks. Considering that even small networks can encode a large number of sub-programs, we select sub-programs that are likely to generalize to unseen tasks. This is achieved through a subset selection procedure that minimizes the Levin loss. Empirical evidence from challenging exploration scenarios in two grid-world domains demonstrates that our methodology can extract helpful programs, thus speeding up the learning process in tasks that are similar and yet distinct from the one used to train the original model.

Bio: Dr. Levi Lelis is an Assistant Professor at the University of Alberta, an Amii Fellow, and a CIFAR AI Chair. Levi’s research is dedicated to the development of principled algorithms to solve combinatorial search problems. These problems are integral to optimizing tasks in various sectors. Levi’s research group is focused on combinatorial search problems arising from the search for programmatic solutions---computer programs written in a domain-specific language encoding problem solutions. Levi believes that the most promising path to creating agents that learn continually, efficiently, and safely is to represent the agents’ knowledge programmatically. While programmatic representations offer many advantages, including modularity and reusability, they present a significant challenge: the need to search over large, non-differentiable spaces not suited for gradient descent methods. Addressing this challenge is the current focus of Levi’s work.

Talk 2 (11:40 am-12:20) Vahid Babaei , Max Planck Institute for Informatics

Title: Inverse Design with Neural Surrogate Models

Abstract: The digitalization of manufacturing is turning fabrication hardware into computers. As traditional tools, such as computer aided design, manufacturing, and engineering (CAD/CAM/CAE) lag behind this new paradigm, the field of computational fabrication has recently emerged from computer graphics to address this knowledge gap with a computer-science mindset. Computer graphics is extremely powerful in creating content for the virtual world. The connection is therefore a natural one as the digital fabrication hardware is starving for innovative content. In this talk, I will focus on inverse design, a powerful paradigm of content synthesis for digital fabrication, which creates fabricable designs given the desired performances. Specifically, I will discuss a class of inverse design problems that deals with data-driven neural surrogate models. These surrogates learn and replace a forward process, such as a computationally heavy simulation.

Bio: Vahid Babaei leads the AI aided Design and Manufacturing group at the Computer Graphics Department of the Max Planck Institute for Informatics in Saarbrücken, Germany. He was a postdoctoral researcher at the Computational Design and Fabrication Group of Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT. He obtained his PhD in Computer Science from EPFL. Vahid Babaei is the recipient of the 2023 Germany-wide Curious Mind Award in the area of ‘AI, Digitalization, and Robotics’, the Hermann Neuhaus Prize of the Max Planck Society, and two postdoctoral fellowships awarded by the Swiss National Science Foundation. He is interested in developing original computer science methods for both engineering design and advanced manufacturing.

Talk 3 (1 pm -1:40 pm) Sven Koenig, computer science department, University of Southern California

Title: Multi-Agent Path Finding and Its Applications

Abstract: The coordination of robots and other agents becomes more and more important for industry. For example, on the order of one thousand robots already navigate autonomously in Amazon fulfillment centers to move inventory pods all the way from their storage locations to the picking stations that need the products they store (and vice versa). Optimal and even some approximately optimal path planning for these robots is NP-hard, yet one must find high-quality collision-free paths for them in real-time. Algorithms for such multi-agent path-finding problems have been studied in robotics and theoretical computer science for a longer time but are insufficient since they are either fast but of insufficient solution quality or of good solution quality but too slow. In this talk, I will discuss different variants of multi-agent path-finding problems, cool ideas for both solving them and executing the resulting plans robustly, and several of their applications. Our research on this topic has been funded by both NSF and Amazon Robotics.

Bio: Sven Koenig is a professor of computer science at the University of Southern California. Most of his current research focuses on planning for single agents (such as robots) or multi-agent systems. Additional information about him can be found on his webpages: idm-lab.org.

Talk 4 (1:40 pm -2:20 pm) Jiaoyang Li, Robotics Institute, Carnegie Mellon University

Title: Layout Design for Large-Scale Multi-Robot Coordination

Abstract: Today, thousands of robots are navigating autonomously in warehouses, transporting goods from one location to another. While numerous planning algorithms are developed to coordinate robots more efficiently and robustly, warehouse layouts remain largely unchanged – they still adhere to the traditional pattern designed for human workers rather than robots. In this talk, I will share our recent progress in exploring layout design and optimization to enhance large-scale multi-robot coordination. I will first introduce a direct layout design method, followed by a method to optimize layout generators instead of layouts. I will then extend these ideas to virtual layout design, which does not require changes to the physical world that robots navigate and thus has the potential for applications beyond automated warehouses.

Bio: Jiaoyang Li is an assistant professor at the Robotics Institute of CMU School of Computer Science. She received her Ph.D. in computer science from the University of Southern California (USC) in 2022. Her research interests lie in the coordination of large robot teams. Her research received recognition through prestigious paper awards (e.g., best student paper, best demo, and best student paper nomination at ICAPS in 2020, 2021, and 2023, along with the best paper finalist at MRS in 2023) and competition championships (e.g., winners of NeurIPS Flatland Challenge in 2020 and Flatland 3 in 2021, as well as the League of Robot Runners sponsored by Amazon Robotics in 2023). Her Ph.D. dissertation also received the best dissertation awards from ICAPS, AAMAS, and USC in 2023.