Bio

I am an incoming Assistant Professor at MIT EECS, where I will be leading the Scene Representation Group. Currently, I am a Postdoc at MIT's CSAIL with Josh Tenenbaum, Bill Freeman, and Fredo Durand. Previously, I finished a Ph.D. at Stanford University. My research interest lies in neural scene representations - the way neural networks learn to represent information on our world. My goal is to allow independent agents to reason about our world given visual observations, such as inferring a complete model of a scene with information on geometry, material, lighting etc. from only few observations, a task that is simple for humans, but currently impossible for AI.

Hiring graduate students!

I am hiring graduate students to join my lab at MIT in July 2022. If you want to push what's possible with neural scene representations, inverse graphics and neural rendering and apply them to problems across computer vision, graphics, and robotics, please apply here - deadline is December 15th 2021!

News

June 2021
I am thrilled to announce that I will be joining MIT as tenure-track assistant professor in July 2022! My lab will investigate neural scene representations, inverse graphics, neural rendering, and their applications in vision, graphics, robotics, and AI.
June 2021
I have created a slideshare account and will start uploading slides for some of my presentations / talks / courses, starting with the slides for the introduction to novel view synthesis at SIGGRAPH 2021. Feel free to re-use them - I only ask that you keep some form of acknowledgement :) Find them here.
January 2021
I published a reading list on neural implicit representations on github that I give students to get started in this area, inspired by the awesome-computer-vision list with extra commentary & notes. Check it out!
January 2021
I am now serving as an academic advisor to Preferred Networks, Inc!
June 2020
I just graduated Stanford with my thesis on Self-supervised Scene Representation Learning. There's a few interesting thoughts in there - especially check out the introduction and conclusion!
March 2020
Our CVPR tutorial on Neural Rendering is on youtube, free to watch for everyone! Here's the link to the morning session - at 2:20:00, I'm giving an overview over Novel View Synthesis. Here's the link to the afternoon session.
November 2019
Our paper "Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations" wins an honorable mention for "Outstanding New Directions" at NeurIPS 2019! Watch my talk here.
May 2019
I will join Prof. Noah Snavely's group at the Google NYC office over the summer and continue working on deep learning for scene understanding and novel view synthesis.

Publications

Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulation
arXiv 2021
Anthony Simeonov*, Yilun Du*, Andrea Tagliasacchi, Alberto Rodriguez, Pulkit Agrawal†, Vincent Sitzmann†
Learning Signal-Agnostic Manifolds of Neural Fields
NeurIPS 2021
Yilun Du, Katherine M. Collins, Joshua Tenenbaum, Vincent Sitzmann
Light Field Networks: Neural Scene Representations with Single-Evaluation Rendering
NeurIPS 2021 (Spotlight)
Vincent Sitzmann*, Semon Rezchikov*, William T. Freeman, Joshua B. Tenenbaum, Frédo Durand
Implicit Neural Representations with Periodic Activation Functions
NeurIPS 2020 (Oral)
Vincent Sitzmann*, Julien N. P. Martel*, Alexander W. Bergman, David B. Lindell, Gordon Wetzstein
MetaSDF: Meta-learning Signed Distance Functions
NeurIPS 2020
Vincent Sitzmann*, Eric R. Chan*, Richard Tucker, Noah Snavely, Gordon Wetzstein
State of the Art on Neural Rendering
Computer Graphics Forum 2020 - EG 2020 (STAR Report)
Ayush Tewari*, Ohad Fried*, Justus Thies*, Vincent Sitzmann*, Stephen Lombardi, Kalyan Sunkavalli, Ricardo Martin-Brualla, Tomas Simon, Jason Saragih, Matthias Nießner, Rohit Pandey, Sean Fanello, Gordon Wetzstein, Jun-Yan Zhu, Christian Theobalt, Maneesh Agrawala, Eli Shechtman, Dan B Goldman, Michael Zollhöfer
Inferring Semantic Information with 3D Neural Scene Representations
3DV
Amit Kohli*, Vincent Sitzmann*, Gordon Wetzstein
Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations
NeurIPS 2019 (Oral, Honorable Mention "Outstanding New Directions")
Vincent Sitzmann, Michael Zollhöfer, Gordon Wetzstein
DeepVoxels: Learning Persistent 3D Feature Embeddings
CVPR 2019 (Oral)
Vincent Sitzmann, Justus Thies, Felix Heide, Matthias Nießner, Gordon Wetzstein, Michael Zollhöfer
Hybrid optical-electronic convolutional neural networks with optimized diffractive optics for image classification
Scientific Reports
Julie Chang, Vincent Sitzmann, Xiong Dun, Wolfgang Heidrich, Gordon Wetzstein
End-to-end Optimization of Optics and Image Processing for Achromatic Extended Depth of Field and Super-resolution Imaging
SIGGRAPH 2018
Vincent Sitzmann*, Steven Diamond*, Yifan Peng*, Xiong Dun, Stephen Boyd, Wolfgang Heidrich, Felix Heide, Gordon Wetzstein
Saliency in VR: How do people explore virtual environments?
IEEE VR 2018
Vincent Sitzmann*, Ana Serrano*, Amy Pavel, Maneesh Agrawala, Belen Masia, Diego Gutierrez, Gordon Wetzstein
Movie Editing and Cognitive Event Segmentation in Virtual Reality Video
SIGGRAPH 2017
Ana Serrano, Vincent Sitzmann, Jaime Ruiz-Borau, Gordon Wetzstein, Diego Gutierrez, Belen Masia
Towards a Machine-learning Approach for Sickness Prediction in 360° Stereoscopic Videos
IEEE VR 2018
Nitish Padmanaban*, Timon Ruban*, Vincent Sitzmann, Anthony M. Norcia, Gordon Wetzstein