Yeonjin Chang

I am a graduate student at Seoul National University. I am advised by Professor Nojun Kwak, as a member of Machine Intelligence and Pattern Analysis Lab (MIPAL). Prior to my graduate studies, I earned a bachelor's degree in Astronomy and Industrial Engineering.

Email / CV / Google Scholar / Github

Research

	ROODI: Reconstructing Occluded Objects with Denoising Inpainters Yeonjin Chang, Erqun Dong, Seunghyeon Seo, Nojun Kwak, Kwang Moo Yi Under Review project page / arXiv We introduce a 3D object extraction method for Gaussian Splatting that prunes irrelevant primitives using K-nearest neighbors analysis and compensates for occlusions with diffusion-based generative inpainting.
	HG-NeRF: Casting an Hourglass as a Bundle of Rays for Few-shot Neural Rendering Seunghyeon Seo, Yeonjin Chang, Jayeon Yoo, Seungwoo Lee, Hojun Lee, Nojun Kwak Under Review We cast an hourglass as an additional training ray, which adaptively regularizes the high-frequency components of the samples, and enhance the integrity of training framework by conceptualizing the hourglass as a bundle of flipped diffuse reflection rays, aligning with the Lambertian assumption.
	Fast Sun-aligned Outdoor Scene Relighting based on TensoRF Yeonjin Chang, Yearim Kim, Seunghyeon Seo, Jung Yi, Nojun Kwak WACV, 2024 arXiv We simplify outdoor scene relighting for NeRF by aligning with the sun, eliminating the need for environment maps and speeding up the process using a novel cubemap concept within the framework of TensoRF.
	FlipNeRF: Flipped Reflection Rays for Few-shot Novel View Synthesis Seunghyeon Seo, Yeonjin Chang, Nojun Kwak ICCV, 2023 project page / arXiv We utilize the flipped reflection rays as additional training resources for the few-shot novel view synthesis, leading to more accurate surface normal estimation.
	MixNeRF: Modeling a Ray with Mixture Density for Novel View Synthesis from Sparse Inputs Seunghyeon Seo, Donghoon Han, Yeonjin Chang, Nojun Kwak CVPR, 2023 project page / arXiv We model a ray with mixture density model, leading to efficient learning of density distribution with sparse inputs, and propose an effective auxiliary task of ray depth estimation for few-shot novel view synthesis.
	Semantics-Guided Object Removal for Facial Images: with Broad Applicability and Robust Style Preservation Jookyung Song, Yeonjin Chang, Seonguk Park, Nojun Kwak ICASSP*, 2023 arXiv Write short description here.

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