Seokhun Choi

I'm a research scientist at LG Electronics, currently working as a member of the Generative AI team. I received B.S. and M.S. degrees in automotive engineering at Hanyang University in 2020 and 2022, respectively.

My research interests lie in the fields of computer vision and generative AI. I'm particularly interested in 2D/3D content generation and manipulation using diffusion models, Neural Radiance Fields (NeRF), 3D Gaussian Splatting (3DGS), and other related methods.

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Overview of blending nerf
Click-Gaussian: Interactive Segmentation to Any 3D Gaussians
Seokhun Choi*, Hyeonseop Song*, Jaechul Kim, Taehyeong Kim, Hoseok Do
*Equal contribution
ECCV 2024 - arXiv / Project / Video

We present Click-Gaussian, a swift and precise method for interactive segmentation of 3D Gaussians using two-level granularity feature fields derived from 2D segmentation masks.
Overview of blending nerf
Blending-NeRF: Text-Driven Localized Editing in Neural Radiance Fields
Hyeonseop Song*, Seokhun Choi*, Hoseok Do, Chul Lee, Taehyeong Kim
*Equal contribution
ICCV 2023 - arXiv / Project / Video / Paper / Poster

Blending-NeRF performs localized 3D editing to a source object by text prompts with explicitly predefined three types of editing operations: color change, density addition, and density removal.
Overview of vehicle speed optimization based on predicted traction torque. Vehicle Speed Optimization Based on Predicted Traction Torque Using Machine Learning
Byunggun Kim, Gihoon Kim, Yoonyong Ahn, Jihoon Sung, Seokhun Choi, Youngho Jun, Kunsoo Huh
Transactions of the Korean Society of Automotive Engineers, 2022 - Paper

We propose vehicle speed optimization method using a network that predicts traction torque, which is trained with real-world vehicle driving data.

Source code for this page was taken from Jon Barron's website.