Hi! I'm Supasorn or Aek (pronounced as EK)! I'm now joining Google Brain as a resident working in a new hybrid field of deep learning / vision / VR. My goal is to bring computer vision out of the lab into the real world and make it really work in the wild. My thesis revolves around the questions [What aspects of a person can you infer by just looking at their photos and videos? Can you model somone's persona and create a digital human that looks, talks, and acts just like them?] In particular, I developed algorithms that solve the problems of how to build a "moving" 3D face model out of just photos, how to create facial textures that can smile with creases and wrinkles just like the real thing, how to capture and transfer expressions, how to generate videos of a person from their voice, and more -- by learning from just existing photos and videos of the person.

I finished my Ph.D. from UW working with Prof. Steve Seitz and Prof. Ira Kemelmacher in Graphics-Vision group GRAIL. I went to Cornell for undergrad, and had a great pleasure working with Prof. John Hopcroft on social graph algorithms, and later got inspired by Prof. Noah Snavely with his computer vision class. I love hacking, coding, tackling hard problems, and I tried very hard to make my solutions to complex problems as simple as possible.


The best way to contact me is through email: my first name at gmail. If I don't reply, feel free to ping me. It's likely I lost it in the pile of 10K+ mails.


Synthesizing Obama:
Learning Lip Sync from Audio

S. Suwajanakorn, S.M. Seitz, I. Kemelmacher-Shlizerman SIGGRAPH 2017
Given audio of President Barack Obama, we synthesize photorealistic video of him speaking with accurate lip sync. Trained on many hours of just video footage from whitehouse.gov, our recurrent neural net approach synthesizes mouth shape and texture from audio, which are composited into a reference video.

What Makes Tom Hanks Look Like Tom Hanks

S. Suwajanakorn, S.M. Seitz, I. Kemelmacher-Shlizerman ICCV 2015
Madrona Prize Winner - GeekWire's Innovation of the Year

We reconstruct a controllable model of a person from a large photo collection that captures his or her persona, i.e., physical appearance and behavior. Our system is based on a novel combination of 3D face reconstruction, tracking, alignment, and multi-texture modeling, applied to the puppeteering problem.
Paper Web

Depth from Focus with Your Mobile Phone

S. Suwajanakorn, C. Hernández, S.M. Seitz CVPR 2015
We introduce the first depth from focus (DfF) method capable of handling images from mobile phones and other hand-held cameras. With this technique, we can automatically generate a depth map for every photo you take with your phone.
Paper Supplement Supp-Video Data U.S. Patent

Total Moving Face Reconstruction

S. Suwajanakorn, I. Kemelmacher-Shlizerman, S.M. Seitz ECCV 2014  - Madrona Prize Runner-Up
Our approach takes a single video of a person's face and reconstructs a high detail 3D shape for each video frame. We target videos taken under uncontrolled and uncalibrated imaging conditions.
Paper Web

Illumination-aware Age Progression

I. Kemelmacher-Shlizerman, S. Suwajanakorn, S.M. Seitz CVPR 2014
We present an approach that takes a single photograph of a child as input and automatically produces a series of age-progressed outputs between 1 and 80 years of age, accounting for pose, expression, and illumination.
Paper Web

Extracting the Core Structure of Social Networks Using (α, β)-Communities.

Liaoruo Wang, John Hopcroft, Jing He, Hongyu Liang, Supasorn Suwajanakorn Internet Mathematics, 2013
We present a heuristic algorithm that in practice finds a fundamental community structure and demonstrate that the core structure in social networks is due to underlying social structure rather than high-degree vertices or degree distribution.

Detecting the Structure of Social Networks Using (α,β)-Communities

Jing He, John Hopcroft, Hongyu Liang, Supasorn Suwajanakorn, Liaoruo Wang 8th Workshop on Algorithms and Models for the Web Graph (WAW) 2011
A talk I gave on my algorithm used in the paper (not in WAW).