CVM 2020 Programme

Conference venue: Online Conference by Tencent/VooV + Bilibili

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Keynote Speakers

Gordon Wetzstein, Stanford University

Title:

Towards Neural Imaging and Display

Abstract:

Computational imaging leverages the co-design of hardware and software to re-define next-generation camera and display systems. In this talk, we discuss recent advances in computational single-photon imaging to enable non-line-of-sight vision and 3D imaging through highly scattering media. We also discuss how the emerging paradigm of end-to-end optimization of optics and imaging processing algorithms unlocks unprecedented capabilities in HDR and depth imaging as well as hybrid optical-electronic computing. Finally, similar end-to-end design strategies also allow us to define next-generation displays, such as holographic near-eye systems for virtual and augmented reality applications, which we will discuss in this talk.

Speaker's Biography:

Gordon Wetzstein is an Assistant Professor of Electrical Engineering and, by courtesy, of Computer Science at Stanford University. He is the leader of the Stanford Computational Imaging Lab and a faculty co-director of the Stanford Center for Image Systems Engineering. At the intersection of computer graphics and vision, computational optics, and applied vision science, Prof. Wetzstein's research has a wide range of applications in next-generation imaging, display, wearable computing, and microscopy systems. Prior to joining Stanford in 2014, Prof. Wetzstein was a Research Scientist in the Camera Culture Group at MIT. He received a Ph.D. in Computer Science from the University of British Columbia in 2011 and graduated with Honors from the Bauhaus in Weimar, Germany before that. He is the recipient of an NSF CAREER Award, an Alfred P. Sloan Fellowship, an ACM SIGGRAPH Significant New Researcher Award, a Presidential Early Career Award for Scientists and Engineers (PECASE), an SPIE Early Career Achievement Award, a Terman Fellowship, an Okawa Research Grant, the Electronic Imaging Scientist of the Year 2017 Award, an Alain Fournier Ph.D. Dissertation Award, and a Laval Virtual Award as well as Best Paper and Demo Awards at ICCP 2011, 2014, and 2016 and at ICIP 2016.

Christian Theobalt, MPII Saarbrucken, Germany

Title:

New methods for Reconstruction and Neural Rendering of Real World Scenes

Abstract:

In this presentation, I will talk about some of the recent work we did on new methods for reconstructing computer graphics models of specific classes of dynamic real world scenes from sparse or even monocular video data. I will also talk about how neural rendering approaches that combine model-based and neural network based concepts for image formation in new ways enable new ways synthesize highly realistic imagery and videos of real work scenes under user control. My talk will exemplify these concepts using work we did on human faces and face video, but will also briefly illustrate how we began to extend these concepts to a broader range of scenes.

Speaker's Biography:

Christian Theobalt is a Professor of Computer Science and the head of the research group "Graphics, Vision, & Video" at the Max-Planck-Institute (MPI) for Informatics, Saarbrucken, Germany. He is also a Professor of Computer Science at Saarland University, Germany. From 2007 until 2009 he was a Visiting Assistant Professor in the Department of Computer Science at Stanford University. He received his MSc degree in Artificial Intelligence from the University of Edinburgh, his Diplom (MS) degree in Computer Science from Saarland University, and his PhD (Dr.-Ing.) from the Max-Planck-Institute for Informatics. In his research he looks at algorithmic problems that lie at the intersection of Computer Graphics, Computer Vision and machine learning, such as: static and dynamic 3D scene reconstruction, marker-less motion and performance capture, virtual and augmented reality, computer animation, appearance and reflectance modelling, intrinsic video and inverse rendering, machine learning for graphics and vision, new sensors for 3D acquisition, advanced video processing, as well as image- and physically-based rendering. He is also interested in using reconstruction techniques for human computer interaction. For his work, he received several awards, including the Otto Hahn Medal of the Max-Planck Society in 2007, the EUROGRAPHICS Young Researcher Award in 2009, the German Pattern Recognition Award 2012, and the Karl Heinz Beckurts Award in 2017. He received two ERC grants, an ERC Starting Grant in 2013 and an ERC Consolidator Grant in 2017. In 2015, he was elected as one of the top 40 innovation leaders under 40 in Germany by the business magazine Capital. Christian Theobalt is also a co-founder of an award-winning spin-off company from his group - www.thecaptury.com - that is commercializing one of the most advanced solutions for marker-less motion capture.

Ruizhen Hu, Shenzhen University

Title:

Shape analysis via functionality and mobility inference

Abstract:

The computational power, especially neural network processing power of mobile devices will be advanced significantly in the near future. Combined with better camera sensors and new camera module designs, it provides an exciting opportunity to revamp the traditional mobile imaging technologies, and brings new and exciting photography experiences to virtually everyone around the world. It is a brand new chapter in the history of photography, and it has never been a better time to work in this area. In this talk, I will introduce a series of new mobile photography technologies and applications that we are developing in-house, many will soon be released to tens of millions of end users worldwide. I will also shed some light on where the industry is going moving forward.
One of the goals of computer graphics is to provide tools for designing and simulating real or imagined artifacts, such as man-made objects. Such artifacts are usually functional, and thus an understanding of functionality is paramount for simulating and validating different designs of artifacts. Moreover, in recent years, computer graphics and related fields, such as computer vision and robotics, have also devoted much attention to the inference of possible motions of 3D objects and their parts, since this problem is closely related to an understanding of object and functionality. In this talk, I will introduce a series of works on analysis and understanding of shape functionality and part mobility with interaction as the key component, and discuss its potential applications in modeling, fabrication and robotics.

Speaker's Biography:

Ruizhen Hu is an Assistant Professor at Shenzhen University, China. She received her Ph.D. from the Department of Mathematics, Zhejiang University. Before that, she spent two years visiting Simon Fraser University, Canada. Her research interests are in computer graphics, with a recent focus on applying machine learning to advance the understanding and generative modeling of visual data including 3D shapes and indoor scenes. She has received several research awards including the Asia Graphics “Young Researcher Award” in 2019, among others.