no code implementations • 12 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.
1 code implementation • 16 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.
no code implementations • 18 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.
no code implementations • 28 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.
1 code implementation • 15 Nov 2021 • Kangyeol Kim, Sunghyun Park, Jaeseong Lee, Sunghyo Chung, Junsoo Lee, Jaegul Choo
We present a novel Animation CelebHeads dataset (AnimeCeleb) to address an animation head reenactment.
1 code implementation • 4 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.
no code implementations • 26 Nov 2018 • Sungsoo Kim, Jin Soo Park, Christos G. Bampis, Jaeseong Lee, Mia K. Markey, Alexandros G. Dimakis, Alan C. Bovik
We propose a video compression framework using conditional Generative Adversarial Networks (GANs).