no code implementations • 23 Jan 2024 • Hyeonwoo Kim, Sookwan Han, Patrick Kwon, Hanbyul Joo
One of the major challenges in AI is teaching machines to precisely respond and utilize environmental functionalities, thereby achieving the affordance awareness that humans possess.
1 code implementation • ICCV 2023 • Byungjun Kim, Patrick Kwon, Kwangho Lee, Myunggi Lee, Sookwan Han, Daesik Kim, Hanbyul Joo
We propose a 3D generation pipeline that uses diffusion models to generate realistic human digital avatars.
no code implementations • 17 May 2023 • Kwangho Lee, Patrick Kwon, Myung Ki Lee, Namhyuk Ahn, Junsoo Lee
To enable this, we introduce a landmark-parameter morphable model (LPMM), which offers control over the facial landmark domain through a set of semantic parameters.
no code implementations • CVPR 2023 • Namhyuk Ahn, Patrick Kwon, Jihye Back, Kibeom Hong, Seungkwon Kim
In the texture decoder, we propose a texture controller, which enables a user to control stroke style and abstraction to generate diverse cartoon textures.
no code implementations • ICCV 2021 • Patrick Kwon, Jaeseong You, Gyuhyeon Nam, Sungwoo Park, Gyeongsu Chae
A variety of effective face-swap and face-reenactment methods have been publicized in recent years, democratizing the face synthesis technology to a great extent.