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Neural Representation and Rendering of 3D Real-world Scenes
Lingjie Liu
IRB 4105- Virtual- https://umd.zoom.us/j/98095131895?pwd=bFRySUJZSytQcjFVVis0dFpuWU1TZz09
Monday, April 4, 2022, 11:00 am-12:00 pm Calendar
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High-quality reconstruction and photo-realistic rendering of real-world scenes are two important tasks that have a wide range of applications in AR/VR, movie production, games, and robotics. These tasks are challenging because real-world scenes contain complex phenomena, such as occlusions, motions and interactions. Approaching these tasks using classical computer graphics techniques is a highly difficult and time-consuming process, which requires complicated capture procedures, manual intervention, and a sophisticated global illumination rendering process. In this talk, I will introduce our recent work that integrates deep learning techniques into the classical graphics pipeline for modelling humans and static scenes in an automatic way. Specifically, I will talk about creating photo-realistic animatable human characters from only RGB videos, high-quality reconstruction and fast novel view synthesis of general static scenes from RGB image inputs, and scene generation with a 3D generative model. Finally, I will discuss challenges and opportunities in this area for future work.


Lingjie Liu is Lise Meitner Postdoctoral Research Fellow working with Prof. Christian Theobalt in the Visual Computing and AI Department at the Max Planck Institute for Informatics. She received her Ph.D. degree at the University of Hong Kong in 2019. Before that, she got her B.Sc. degree in Computer Science at Huazhong University of Science and Technology in 2014. Her research interests include neural scene representations, neural rendering, human performance modeling and capture, and 3D reconstruction. Webpage: https://lingjie0206.github.io/.

This talk is organized by Richa Mathur