GeonU Kim

gukim@postech.ac.kr

I am a master’s student at AMILab in Grad. School of Artificial Intelligence at POSTECH, supervised by Prof. Dr. Tae-Hyun Oh. I received my bachelor's degree in Computer Science from UNIST.

CV / Google Scholar

Research Interestes

3D computer vision
Generative AI

Publications

	FPGS: Feed-Forward Semantic-aware Photorealistic Style Transfer of Large-Scale Gaussian Splatting GeonU Kim, Kim Youwang, Lee Hyoseok, Tae-Hyun Oh, Under review project page / arXiv Feed-forward Gaussian Splatting photorealistic style transfer, with semantic correspondence.
	Dr. Splat: Directly Referring 3D Gaussian Splatting via Direct Language Embedding Registration Kim Jun-Seong, GeonU Kim, Kim Yu-Ji, Yu-Chiang Frank Wang, Jaesung Choe, Tae-Hyun Oh, CVPR, 2025 project page / arXiv Direct 3D feature embedded Gaussian with product quantization.
	Factorized Multi-Resolution HashGrid for Efficient Neural Radiance Fields: Execution on Edge-Devices Kim Jun-Seong, Mingyu Kim, GeonU Kim, Tae-Hyun Oh, Jin-Hwa Kim, RA-L, 2024 project page / paper Joint modeling of Hash encoding and multi-resolusion tri-plane for memory efficient neural radiance fields.
	FPRF: Feed-Forward Photorealistic Style Transfer of Large-Scale 3D Neural Radiance Fields GeonU Kim, Kim Youwang, Tae-Hyun Oh, AAAI, 2024 project page / arXiv Stylize neural radiance fields with multiple style refernece images, in a feed-forward manner.
	Multi-Person 3D Pose and Shape Estimation via Inverse Kinematics and Refinement Junuk Cha, Muhammad Saqlain, GeonU Kim, Mingyu Shin, Seungryul Baek, ECCV, 2022 arXiv / code Reconstruct multi-person human meshes from a single image with inverse kinematics and relation-aware refinement.

This website is based on this page. We thank Jon Barron for kindly open-sourcing the source code.