Cheng-Kang Ted Chao 趙城綱

I am a fourth-year Computer Science PhD student in CraGL at George Mason University, where I have wonderful time working with Yotam Gingold. Throughout my PhD journey, I worked as a research intern under Jose Echevarria during my internship at Adobe Research. I also was an applied research intern at Tencent Pixel Lab, working with Ran Zhang and Changxi Zheng.

Currently, I am a research intern at Adobe Research working with Li-Yi Wei and Rubaiat Habib Kazi.

Before moving to Virginia, I was a Master's student in Computational Science and Engineering at Georgia Institute of Technology, advised by Edmond Chow. I received my Bachelor's degree in Mathematics from National Central University, Taiwan.

Email  /  CV  /  Github

profile photo

I focus on developing efficient algorithms for image and shape manipulation to address various design challenges, such as stylization and editing. I am particularly interested in leveraging a combination of optimization, machine learning, computer vision, partial differential equations, and numerical linear algebra to achieve this goal.

ColorfulCurves: Palette-Aware Lightness Control and Color Editing via Sparse Optimization
Cheng-Kang Ted Chao, Jason Klein, Jianchao Tan, Jose Echevarria, Yotam Gingold
ACM Transactions on Graphics (TOG). Presented at SIGGRAPH North America, 2023
paper / demo video / code

We developed a constraint-driven sparse optimization for color editing, uniting palette-based framework with lightness manipulation.

LoCoPalettes: Local Control for Palette-based Image Editing
Cheng-Kang Ted Chao, Jason Klein, Jianchao Tan, Jose Echevarria, Yotam Gingold
Computer Graphics Forum (CGF). Presented at Eurographics Symposium on Rendering (EGSR) , 2023
paper / demo video / code

We extended palette-based editing framework to utilizing semantic soft segments within our proposed palette hierarchy. We also developed an approach to compute sparser weights without sacrificing much spatial coherence.

Text-guided Image-and-Shape Editing and Generation: A Short Survey
Cheng-Kang Ted Chao, Yotam Gingold
arXiv, 2023

We wrote a short survey paper summarizing some state-of-the-art (mainly focused within recent two years) editing and generation algorithms for images and shapes.

PosterChild: Blend‐Aware Artistic Posterization
Cheng-Kang Ted Chao, Karan Singh, Yotam Gingold
Computer Graphics Forum (CGF). Special issue for Eurographics Symposium on Rendering (EGSR), 2021
project page / video / code

Automatic artistic posterization with user's control for different levels of abstraction, color diversity, and image recoloring.


 May.28.2024:   I started as a research intern at Adobe Research in San Jose.
 Aug.09.2023:   I presented our work ColorfulCurves at SIGGRAPH 2023 in Los Angeles.
June.30.2023:   I gave a talk at EGSR 2023 in TU Delft on our work LoCoPalettes.
June.20.2023:   I started as an applied research intern at the Tencent Pixel Lab in New York.
May. 25.2023:   I attended Capital Graphics 2023.


I found the below are pretty useful for research use, especially when you need to quickly work on mathematical derivations but forgot some of the rules...

1.   The Matrix Cookbook: a very powerful book having different kinds of derivatives lookup
2.   Matrix Calculus: an interactive tool for computing derivatives for optimization problems

Graduate Teaching Assistant, CS600 Theory of Computation, Fall 2022, 2023
Graduate Teaching Assistant, CS688 Machine Learning, Spring 2022

I enjoy taking black-and-white photography when traveling. Check out my instagram for more photos.

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