2nd Workshop on Scalable 3D Scene Generation and Geometric Scene Understanding

Kristen Grauman is a Professor at the University of Texas at Austin and a Research Director in Facebook AI Research (FAIR). Her research in computer vision and machine learning focuses on video, visual recognition, and action for perception or embodied AI. She and her collaborators have been recognized with several Best Paper awards in computer vision, including a 2011 Marr Prize and a 2017 Helmholtz Prize (test of time award). She served for six years as an Associate Editor-in-Chief for the TPAMI and for ten years as an Editorial Board member for IJCV. She also served as a Program Chair of CVPR2015, NeurIPS2018, and ICCV2023.

Angel Xuan Chang is an Associate Professor at Simon Fraser University. Prior to this, she was a visiting research scientist at Facebook AI Research and a research scientist at Eloquent Labs working on dialogue. She received my Ph.D. in Computer Science from Stanford, where she was part of the Natural Language Processing Group and advised by Chris Manning. Her research focuses on connecting language to 3D representations of shapes and scenes and grounding of language for embodied agents in indoor environments. She has worked on methods for synthesizing 3D scenes and shapes from natural language, and various datasets for 3D scene understanding. In general, she is interested in the semantics of shapes and scenes, the representation and acquisition of common sense knowledge, and reasoning using probabilistic models.

Vincent Sitzmann is an Assistant Professor at MIT EECS, where he is leading the Scene Representation Group.Previously, he did my Ph.D. at Stanford University as well as a Postdoc at MIT CSAIL. His research interest lies in building AI that perceives and models the world the way that humans do. Specifically, he works towards models that can learn to reconstruct a rich state description of their environment, such as reconstructing its 3D structure, materials, semantics, etc. from vision. These models should also be able to model the impact of their own actions on that environment, i.e., learn a "mental simulator" or "world model". He is also particularly interested in models that can learn these skills fully self-supervised only from video and by self-directed interaction with the world.

Amir Zamir is Is a tenure-track Assistant Professor of Computer Science at the Swiss Federal Institute of Technology Lausanne (EPFL). Before EPFL, he was at UC Berkeley, Stanford, and UCF where he had the opportunity of working with Silvio Savarese, Jitendra Malik, Mubarak Shah, Rahul Sukthankar, and Leonidas Guibas. His research interests are broadly in computer vision, machine learning, and AI. The goal of his research has been going beyond narrow and passive vision methods toward a general multi-modal/multi-task vision and models that can operates as part of an active agent in the real world. Here are some of his works on this topic: 4M, Taskonomy, Mid-Level Vision, Gibson, Visual Morphology, MultiMAE, and Omnidata. He was the CVML chief scientist of Aurora Solar (a Forbes AI 50 company, valued at $4B in 2022) from 2015 to 2022.