Taesung Park

I am currently working at a startup as a co-founder. Hope to share more details soon!

Previously, I was a Research Scientist at Adobe Research, focusing on image editing using generative models. I received Ph.D. in Computer Science at UC Berkeley, advised by Prof. Alexei Efros. Previously I interned at Adobe in 2019, working with Richard Zhang, and at NVIDIA, working with Ming-Yu Liu in summer 2018. I received B.S. in Mathematics and M.S. in Computer Science, both at Stanford University. During my Master’s program, I was advised by Vladlen Koltun and Sergey Levine. I was funded by Samsung Scholarship for my Ph.D. study, and a recipient of Adobe Research Fellowship 2020.

Email / Google Scholar

Software

Highlighted softwares developed from my research papers.

	Firefly and Generative Fill is a text-to-image generative model that is trained on ethically sourced data, and is significantly faster than competitors in generating high-resolution images. I developed the upscaling pipeline of Firefly.
	Photoshop Landscape Mixer transforms landscape images in various ways. Contributes to Adobe’s nomination as World’s 4th Most Innovative AI company of 2022. Based on Swapping Autoencoder.
	GauGAN turns sketches into photos. 100 Greatest Innovations of 2019 by Popular Science. Based on Semantic Image Synthesis with Spatially-Adaptive Normalization.

Research

I am mainly interested in image editing and image synthesis using machine learning.

	One-step Diffusion with Distribution Matching Distillation Tianwei Yin, Michaël Gharbi, Richard Zhang, Eli Shechtman, Frédo Durand, Bill Freeman, Taesung Park CVPR, 2024 arXiv / Project
	Holistic Evaluation of Text-To-Image Models Tony Lee, Michihiro Yasunaga, Chenlin Meng, ... Taesung Park, ... Percy Liang, NeurIPS, 2023* arXiv / Project
	Expressive Text-to-Image Generation with Rich Text Songwei Ge, Taesung Park, Jun-Yan Zhu, Jia-Bin Huang, ICCV, 2023 arXiv / Project
	Scaling up GANs for Text-to-Image Synthesis Minguk Kang, Jun-Yan Zhu, Richard Zhang, Jaesik Park, Eli Shechtman, Sylvain Paris, Taesung Park CVPR, 2023 (Highlight) arXiv / Project
	Domain Expansion of Image Generators Yotam Nitzan, Michaël Gharbi, Richard Zhang, Taesung Park, Jun-Yan Zhu, Daniel Cohen-Or, Eli Shechtman CVPR, 2023 arXiv / Project
	BlobGAN: Spatially Disentangled Scene Representations Dave Epstein, Taesung Park, Richard Zhang, Eli Shechtman, Alexei Efros ECCV, 2022 arXiv / Project / Talk / Code / Demo
	ASSET: Autoregressive Semantic Scene Editing with Transformers at High Resolutions Difan Liu, Sandesh Shetty, Tobias Hinz, Matthew Fisher, Richard Zhang, Taesung Park, Evangelos Kalogerakis SIGGRAPH - Journal Track, 2022 PDF(low-res) / PDF(high-res) / Project
	Contrastive Feature Loss for Image Prediction Alex Andonian, Taesung Park, Bryan Russell, Phillip Isola, Jun-Yan Zhu, Richard Zhang ICCVW, 2021 Paper
	Swapping Autoencoder for Deep Image Manipulation Taesung Park, Jun-Yan Zhu, Oliver Wang, Jingwan Lu, Eli Shechtman, Alexei Efros, Richard Zhang NeurIPS, 2020 arXiv / Project
	Contrastive Learning for Unpaired Image-to-Image Translation Taesung Park, Alexei Efros, Richard Zhang Jun-Yan Zhu ECCV, 2020 arXiv / Project / Code
	Semantic Image Synthesis with Spatially-Adaptive Normalization Taesung Park, Ming-Yu Liu, Ting-Chun Wang, Jun-Yan Zhu CVPR, 2019. Best Paper Finalist. SIGGRAPH RTL Best of Show award arXiv / Project / Code
	CyCADA: Cycle-Consistent Adversarial Domain Adaptation Judy Hoffman, Eric Tzeng, Taesung Park, Jun-Yan Zhu, Phillip Isola, Kate Saenko, Alexei Efros, Trevor Darrell ICML, 2018 arXiv / Code
	Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks Jun-Yan Zhu, Taesung Park, Phillip Isola, Alexei Efros ICCV, 2017 (Spotlight, * indicates equal contribution) arXiv / Project / Code
	Inverse Optimal Control for Humanoid Locomotion Taesung Park, Sergey Levine RSS Workshop, 2013 Paper

Dissertation

Machine Learning for Deep Image Synthesis
Taesung Park
EECS Department, UC Berkeley, 2021
Paper

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