Taesung Park

I am a co-founder at Reve.

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.

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

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