I am a Research Scientist at Apple AI/ML. I am also an affiliate associate professor of Computer Science and Engineering at the University of Washington. My research combines deep learning, computer graphics, and computer vision to create usable systems that allow people to better capture, understand, edit, and visualize the world.
I collaborate with the University of Washington Graphics and Imaging Laboratory, Reality Lab, and Facial Expression Research Group where we combine artistic insight and deep learning techniques to better understand facial expressions and create amazing tools for artists.
Previously, I was a Research Scientist at Zillow where I helped create the state of the art in home interior capture and visualization. As a researcher at Amazon, I focused on 3D object creation from photos. My Ph.D. work focused on interactive image-based modeling systems for architectural structures. I spent 10 years at Microsoft Research working on a variety of projects ranging from creating a platform for building virtual worlds to implementing microphone arrays.
Generative Multiplane Images (GMPI) ECCV 2022 oral
Xiaoming Zhao, Fangchang Ma, David Güera Cobo, Zhile Ren, Alexander G. Schwing, and Alex Colburn.
"What is really needed to make an existing 2D GAN 3D-aware?"
To answer this question, we modify a classical GAN, i.e., StyleGANv2, as little as possible. We find that only two modifications are absolutely necessary:
A multiplane image style generator branch which produces a set of alpha maps conditioned on their depth;
A pose-conditioned discriminator.
Fast and Explicit Neural View Synthesis
Pengsheng Guo, Miguel Angel Bautista, Alex Colburn, Liang Yang, Daniel Ulbricht, Joshua M. Susskind, and Qi Shan
WACV 2022: https://arxiv.org/pdf/2107.05775.pdf
We study the problem of novel view synthesis from sparse source observations of a scene comprised of 3D objects. We propose a simple yet effective approach that is neither continuous nor implicit, challenging recent trends on view synthesis
Deep Neural Network Approach for Annual Luminance Simulations,
Yue Liu, Alex Colburn and Mehlika Inanici. Journal of Building Performance Simulation 13(5), August 23, 2020, https://www.tandfonline.com/doi/full/10.1080/19401493.2020.1803404.
Annual luminance maps provide meaningful evaluations for occupants’ visual comfort and perception. This paper presents a novel data-driven approach for predicting annual luminance maps from a limited number of point-in-time high-dynamic-range imagery by utilizing a deep neural network. A sensitivity analysis is performed to develop guidelines for determining the minimum and optimum data collection periods for generating accurate maps.
Emilien Dupont, Miguel Angel Bautista, Alex Colburn, Aditya Sankar, Carlos Guestrin, Josh Susskind, Qi Shan; ICML June 13, 2020. https://proceedings.icml.cc/static/paper_files/icml/2020/2167-Paper.pdf
We propose a framework for learning neural scene representations directly from images, without 3D supervision. Our key insight is that 3D structure can be imposed by ensuring that the learned representation transforms like a real 3D scene.
3D Manhattan Room Layout Reconstruction from a Single 360 Image
Predicting Annual Equirectangular Panoramic Luminance Maps Using Deep Neural Networks
Yue Liu, Alex Colburn and and Mehlika Inanici. To be presented at: 16th IBPSA International Conference and Exhibition, Building Simulation 2019.
Computing Long-term Daylighting Simulations from High Dynamic Range Imagery Using Deep Neural Networks
Yue Liu, Alex Colburn, and Mehlika Inanici, Building Performance Analysis Conference and SimBuild 2018
Learning to Generate 3D Stylized Character Expressions from Humans
Deepali Aneja, Bindita Chaudhuri, Alex Colburn, Gary Faigin, Linda G. Shapiro, Barbara Mones, WACV 2018
Computing Long-term Daylighting Simulations from High Dynamic Range Photographs Using Deep Neural Networks
Deepali Aneja, Alex Colburn, Gary Faigin, Linda Shapiro, and Barbara Mones.
ACCV, 2016. Springer Lecture Notes in Computer Science (Oral presentation).
Alex Colburn, Aseem Agarwala, Aaron Hertzman, Brian Curless, Michael Cohen.
IEEE Transactions on Visualization and Computer Graphics.
Koppal, S.J.; Zitnick, C.L.; Cohen, M.; Sing Bing Kang; Ressler, B.; Colburn, A.;
Computer Graphics and Applications, IEEE , vol.31, no.1, pp.20-35, Jan.-Feb. 2011
Colin Zheng, Alex Colburn, Aseem Agarwala, Maneesh Agrawala, Brian Curless, David Salesin, Michael Cohen.
Graphics Interface, May 2009 (awarded Best Student Paper)
Jue Wang, Pravin Bhat, R. Alex Colburn, Maneesh Agrawala, Michael F. Cohen,
ACM Transactions on Graphics (Proceedings of SIGGRAPH 2005), July 2005
Aseem Agarwala, Mira Dontcheva, Maneesh Agrawala, Steven Drucker, Alex Colburn, Brian Curless, David Salesin, Michael Cohen,
ACM Transactions on Graphics (Proceedings of SIGGRAPH 2004), August 2004
Michael F. Cohen; Alex Colburn; Steven Drucker,
Microsoft Research Tech Report: MSR-TR-2003-40. July 2003
Ross Cutler, Yong Rui, Anoop Gupta, JJ Cadiz, Ivan Tashev, Li-wei He, Alex Colburn, Zhengyou Zhang, Zicheng Liu, Steve Silverberg.
Proceedings of the 2002 ACM International Conference on Multimedia.
Klein, Allison W. ; Sloan, Peter-Pike J. ; Colburn, Alex ; Finkelstein, Adam ; Cohen, Michael F.
Microsoft Research Tech Report: MSR-TR-2001-45, April 2001
R. Alex Colburn; Michael F. Cohen; Steven M. Drucker; Anoop Gupta; Scott LeeTiernan
Microsoft Research Tech Report:MSR-TR-2001-95, October 01 2001
The Role of Eye Gaze in Avatar Mediated Conversational Interfaces
Colburn, R. Alex; Cohen, Michael F. ; Drucker, Steven M.
ACM Sketches and Applications (ACM SIGGRAPH Sketches), 2000
Microsoft Research Tech Report: MSR-TR-2000-81, July 2000
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UW CSE Band AKA the Parody Bits
The UW CSE Band
Introduces basic foundations of research in science and technology. Builds research skills by reading and evaluating papers along with designing and implementing research projects related to Animation, Graphics, and Machine Learning.