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.  


Alex Colburn





Generative Multiplane Images (GMPI) ECCV 2022 oral

Xiaoming Zhao
Fangchang MaDavid Güera CoboZhile RenAlexander 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:

  1. A multiplane image style generator branch which produces a set of alpha maps conditioned on their depth;

  2. 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.

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

LayoutNet: Reconstructing the 3D Room Layout from a Single RGB Image

Chuhang Zou, Alex Colburn, Qi Shan, Derek Hoiem, CVPR 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

Yue Liu, Mehlika Inanici, and Alex Colburn, Radiance Workshop 2017, Portland OR.

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. 

Bundle adjuster updates to support fisheye lens

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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. 

I ran a music seminar in grad school - Computers & Music.

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