Associate Professor, MIT EECS
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Thoughts on research, teaching, and AI. View all posts
Computer vision conventionally maps images to intermediate representations (class, segmentation, 3D reconstruction, etc), but the "real" role of vision has always been as part of a perception-action loop. The LLM moment for vision won't mean SOTA on intermediate tasks, but instead intelligent, embodied agents. World models are the first glimpse. 3D in particular will become obsolete for training embodied intelligence models.
There are two disparate skillsets required for good research: moving fast to find what works, then slowing down to prove it. Knowing when to switch between them is crucial.
Courses I have taught at MIT.
Selected talks and presentations.
Vincent Sitzmann is an Associate Professor at MIT where he leads the Scene Representation Group. He received his PhD at Stanford University with Gordon Wetzstein and his Bachelor's degree from the Technical University of Munich. Vincent's research goal is to build embodied artificial general intelligence - essentially, robots that can inhabit our world alongside humans, that explore it on their own volition, and that - just like humans - can expand their understanding of our world by interacting with it and learning from their interactions. In the past, Vincent's research has made contributions to generative modeling, computer graphics, and robotics, pioneering topics such as differentiable rendering for 3D reconstruction, neural implicit representations, and video generative models for world models and robotics. Vincent has been awarded with numerous awards, such as the MIT Junior Bose Award for excellence in teaching, the NSF CAREER award, and the TC-PAMI Young Researcher award.
