Sunmin Lee

Hello! I am a Ph.D candidate at Seoul National University.

I am a member of Intelligent Motion Lab led by Jungdam Won.
Previously, I was advised by Jehee Lee at Movement Research Lab.

My primary research interest is about understanding human movements and creating a virtual agent that moves like us. It includes, but is not limited to, character animation, machine learning and robotics.

News

Jul 2024	[Code]	Code for our paper SAME is released. Please check it out!
May 2024	[Award]	I’m honored to be selcted as one of WiGRAPH Rising Stars in Computer Graphics. See you in Denver at SIGGRAPH!
Aug 2023	[Paper]	Our Paper SAME is accepted to Siggraph Asia 2023. See you in Sydney!
Jul 2023	[Grant]	I’m honored to recieve ACM Siggraph Conference Grant.
Jun 2023 - Nov 2023	[Intern]	Excited to start a return internship at Meta Realitity Labs Research.
Apr 2023	[Paper]	Our Paper QuestEnvSim is accepted to Siggraph 2023. See you in LA!
Aug 2022 - Feb 2023	[Intern]	Had wonderful six months at Meta Realitity Labs Research

Publications

SAME: Skeleton-Agnostic Motion Embedding for Character Animation

Sunmin Lee, Taeho Kang, Jungnam Park, Jehee Lee, and Jungdam Won

ACM SIGGRAPH ASIA 2023 Conference Proceedings

Project Paper Code Video Bib

@article{10.1145/3610548.3618206,
  author = {Lee, Sunmin and Kang, Taeho and Park, Jungnam and Lee, Jehee and Won, Jungdam},
  title = {SAME: Skeleton-Agnostic Motion Embedding for Character Animation},
  year = {2023},
  doi = {10.1145/3610548.3618206},
  booktitle = {SIGGRAPH Asia 2023 Conference Papers},
  location = {Sydney, Australia},
  paper_link = {https://dl.acm.org/doi/10.1145/3610548.3618206},
}

QuestEnvSim: Environment-Aware Simulated Motion Tracking from Sparse Sensors

Sunmin Lee, Sebastian Starke, Yuting Ye, Jungdam Won, and Alexander Winkler

ACM SIGGRAPH 2023 Conference Proceedings

Project Paper Video Bib

@article{10.1145/3588432.3591504,
  author = {Lee, Sunmin and Starke, Sebastian and Ye, Yuting and Won, Jungdam and Winkler, Alexander},
  title = {QuestEnvSim: Environment-Aware Simulated Motion Tracking from Sparse Sensors},
  year = {2023},
  doi = {10.1145/3588432.3591504},
  booktitle = {SIGGRAPH 2023 Conference Papers},
  location = {Los Angeles, USA},
}

Learning a family of motor skills from a single motion clip

Seyoung Lee, Sunmin Lee, Yongwoo Lee, and Jehee Lee

ACM Transactions on Graphics (SIGGRAPH 2021)

Project Paper Code Video Bib

@article{Lee:2021:Parameterized,
  author = {Lee, Seyoung and Lee, Sunmin and Lee, Yongwoo and Lee, Jehee},
  title = {Learning a family of motor skills from a single motion clip},
  journal = {ACM Trans. Graph.},
  volume = {40},
  number = {4},
  year = {2021},
  articleno = {93},
}

Learning Time-Critical Responses for Interactive Character Control

Kyungho Lee, Sehee Min, Sunmin Lee, and Jehee Lee

ACM Transactions on Graphics (SIGGRAPH 2021)

Project Paper Code Video Bib

@article{Lee:2021:Agile,
  author = {Lee, Kyungho and Min, Sehee and Lee, Sunmin and Lee, Jehee},
  title = {Learning Time-Critical Responses for Interactive Character Control},
  journal = {ACM Trans. Graph.},
  volume = {40},
  number = {4},
  year = {2021},
  articleno = {147},
}

Learning Predict-and-Simulate Policies From Unorganized Human Motion Data

Soohwan Park, Hoseok Ryu, Seyoung Lee, Sunmin Lee, and Jehee Lee

ACM Transactions on Graphics (SIGGRAPH ASIA 2019)

Project Paper Code Video Bib

@article{Park:2019,
  author = {Park, Soohwan and Ryu, Hoseok and Lee, Seyoung and Lee, Sunmin and Lee, Jehee},
  title = {Learning Predict-and-Simulate Policies From Unorganized Human Motion Data},
  journal = {ACM Trans. Graph.},
  volume = {38},
  number = {6},
  year = {2019},
  articleno = {205},
}

FURL

FURL: Fixed-memory and uncertainty reducing local triangle counting for multigraph streams

Minsoo Jung, Yongsub Lim, Sunmin Lee, and U. Kang

Data Mining and Knowledge Discovery (DMKD) 2019

Project Paper Bib

@article{datamine/JungLLK19,
  author = {Jung, Minsoo and Lim, Yongsub and Lee, Sunmin and Kang, U.},
  title = {{FURL:} Fixed-memory and uncertainty reducing local triangle counting
                 for multigraph streams},
  journal = {Data Min. Knowl. Discov.},
  volume = {33},
  number = {5},
  pages = {1225--1253},
  year = {2019},
  url = {https://doi.org/10.1007/s10618-019-00630-6},
  timestamp = {Mon, 12 Aug 2019 16:40:56 +0200},
  biburl = {https://dblp.org/rec/bib/journals/datamine/JungLLK19},
  bibsource = {dblp computer science bibliography, https://dblp.org},
}