Search Results for author: Jaeseong Lee

Found 8 papers, 3 papers with code

Effective Rank Analysis and Regularization for Enhanced 3D Gaussian Splatting

no code implementations17 Jun 2024 Junha Hyung, Susung Hong, Sungwon Hwang, Jaeseong Lee, Jaegul Choo, Jin-Hwa Kim

3D reconstruction from multi-view images is one of the fundamental challenges in computer vision and graphics.

3D Reconstruction

SelfSwapper: Self-Supervised Face Swapping via Shape Agnostic Masked AutoEncoder

no code implementations12 Feb 2024 Jaeseong Lee, Junha Hyung, SOHYUN JEONG, Jaegul Choo

The majority of previous face swapping approaches have relied on the seesaw game training scheme, which often leads to the instability of the model training and results in undesired samples with blended identities due to the target identity leakage problem.

Face Swapping

Expression Domain Translation Network for Cross-domain Head Reenactment

1 code implementation16 Oct 2023 Taewoong Kang, Jeongsik Oh, Jaeseong Lee, Sunghyun Park, Jaegul Choo

Specifically, to maintain the geometric consistency of expressions between the input and output of the expression domain translation network, we employ a 3D geometric-aware loss function that reduces the distances between the vertices in the 3D mesh of the human and anime.

Translation

PixelHuman: Animatable Neural Radiance Fields from Few Images

no code implementations18 Jul 2023 Gyumin Shim, Jaeseong Lee, Junha Hyung, Jaegul Choo

In this paper, we propose PixelHuman, a novel human rendering model that generates animatable human scenes from a few images of a person with unseen identity, views, and poses.

RobustSwap: A Simple yet Robust Face Swapping Model against Attribute Leakage

no code implementations28 Mar 2023 Jaeseong Lee, Taewoo Kim, Sunghyun Park, Younggun Lee, Jaegul Choo

However, we observed that previous approaches still suffer from source attribute leakage, where the source image's attributes interfere with the target image's.

Attribute Face Swapping

S3NAS: Fast NPU-aware Neural Architecture Search Methodology

1 code implementation4 Sep 2020 Jaeseong Lee, Duseok Kang, Soonhoi Ha

In this paper, we present a fast NPU-aware NAS methodology, called S3NAS, to find a CNN architecture with higher accuracy than the existing ones under a given latency constraint.

Image Classification Neural Architecture Search

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