GAMES Webinar 2021 – 197期(海外专题) | Jia-Bin Huang (Facebook Reality Labs)

【GAMES Webinar 2021-197期】(海外专题,Talk+Panel形式)

报告嘉宾:Jia-Bin Huang (Facebook Reality Labs)


报告题目:Learning to See the 3D world


Cameras allow us to effortlessly capture the visual world around us and share memorable moments of our lives. While current computer vision systems work remarkably well on recognizing 2D patterns in images, they often have difficulty recovering the complete 3D geometry of dynamic scenes. On the other hand, humans can perceive complex dynamic scenes in terms of physical surfaces, objects, and scenes in 3D and imagine plausible scene appearances from novel viewpoints. In this talk, I will present my research on reconstructing and rendering our 3D world. Specifically, I will present our work on creating compelling 3D photography from a single image, estimating dense, geometrically consistent depth from casually captured cellphone videos, and learning neural implicit representations for free-viewpoint videos. The core idea is to integrate constraints from the physical model behind the visual observations into learning-based algorithms. I will highlight future challenges and plans for building intelligent machines that learn to see, recreate, and interact with the 3D world.


Jia-Bin Huang is a Research Scientist at Facebook Reality Labs and an incoming faculty in the Department of Computer Science at the University of Maryland, College Park. He received his Ph.D. degree from the Department of Electrical and Computer Engineering at the University of Illinois, Urbana-Champaign. His research interests include computer vision, computer graphics, and machine learning with a focus on visual analysis and synthesis with physically grounded constraints. His research received the best student paper award in IAPR International Conference on Pattern Recognition (ICPR) and the best paper award in the ACM Symposium on Eye Tracking Research & Applications (ETRA). Huang is the recipient of the Dissertation Completion Fellowships, Thomas & Margaret Huang award from UIUC, NSF CRII award, Samsung Global Outreach Award, 3M non-tenured faculty award, and a Google faculty research award.



Jun-Yan Zhu is an Assistant Professor with The Robotics Institute in the School of Computer Science of Carnegie Mellon University. He also holds affiliated faculty appointments in the Computer Science Department and Machine Learning Department. He studies computer vision, computer graphics, machine learning, and computational photography, with the goal of building intelligent machines, capable of recreating our visual world.

Prior to joining CMU, He was a Research Scientist at Adobe Research. He did a postdoc at MIT CSAIL, working with William T. Freeman, Josh Tenenbaum, and Antonio Torralba. He obtained his Ph.D. from UC Berkeley, under the supervision of Alexei A. Efros. He received his B.E. from Tsinghua University, working with Zhuowen Tu, Shi-Min Hu, and Eric Chang.


廖菁博士,现任香港城市大学助理教授。她在香港科技大学和浙江大学取得博士学位,毕业后曾在微软亚洲研究院担任研究员。她的主要研究方向包括计算机图形学,计算机视觉,图像和视频处理等;在CCF A类的期刊和会议上(包括SIGGRAPH, CVPR, ICCV, NeurIPS, ACM TOG, IEEE TPAMI, IEEE TVCG 等)发表论文40余篇;公开专利七项,并成功将专利技术转化到小冰,Skype,Pix等微软产品中。

Cecilia Zhang is a researcher in the computational photography team led by Marc Levoy at Adobe. She received her Ph.D. in Computer Science from UC Berkeley in 2020, advised by Ren Ng. Her research interests are in computational photography, image-based rendering and machine learning. She’s particularly interested in building computational tools to improve the experience of mobile photography. Her works are mainly published in CVPR and SIGGRAPH. She served as the technical committee member for SIGGRAPH ASIA 2021. She received her B.S. in Electrical and Computer Engineering from Rice University in 2015, awarded with Summa Cum Laude and Distinction in Research.

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