Hi! I'm a grad student in CSE at UW working with Prof. Steve Seitz and Prof. Ira Kemelmacher in Graphics-Vision group GRAIL. My main interest is computer vision / graphics, but also includes computational photography, machine learning, and optimization. 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, and tackling hard problems, and I think computer vision is fun because I get to see why on earth eigenvector is useful.
I'm looking for highly motivated students to collaborate with. If you'd like to get involved in a research project, please shoot me an email!
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.
S. Suwajanakorn, S.M. Seitz, I. Kemelmacher-Shlizerman
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.
S. Suwajanakorn, C. Hernández, S.M. Seitz
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
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.
I. Kemelmacher-Shlizerman, S. Suwajanakorn, S.M. Seitz
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.
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.
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).