1 code implementation • 28 Mar 2024 • Seyeon Kim, Siyoon Jin, JiHye Park, Kihong Kim, Jiyoung Kim, Jisu Nam, Seungryong Kim
AToM excels in capturing subtle lip movements by leveraging an audio attention mechanism.
1 code implementation • 15 Feb 2024 • Jisu Nam, Heesu Kim, Dongjae Lee, Siyoon Jin, Seungryong Kim, Seunggyu Chang
The objective of text-to-image (T2I) personalization is to customize a diffusion model to a user-provided reference concept, generating diverse images of the concept aligned with the target prompts.
1 code implementation • 30 May 2023 • Jisu Nam, Gyuseong Lee, Sunwoo Kim, Hyeonsu Kim, Hyoungwon Cho, Seyeon Kim, Seungryong Kim
The objective for establishing dense correspondence between paired images consists of two terms: a data term and a prior term.
1 code implementation • 27 Dec 2022 • Kihong Kim, Yunho Kim, Seokju Cho, Junyoung Seo, Jisu Nam, Kychul Lee, Seungryong Kim, Kwanghee Lee
In this paper, we propose a diffusion-based face swapping framework for the first time, called DiffFace, composed of training ID conditional DDPM, sampling with facial guidance, and a target-preserving blending.
1 code implementation • 6 Oct 2022 • Sunghwan Hong, Jisu Nam, Seokju Cho, Susung Hong, Sangryul Jeon, Dongbo Min, Seungryong Kim
Existing pipelines of semantic correspondence commonly include extracting high-level semantic features for the invariance against intra-class variations and background clutters.
1 code implementation • 22 Jul 2022 • Sunghwan Hong, Seokju Cho, Jisu Nam, Stephen Lin, Seungryong Kim
However, the tokenization of a correlation map for transformer processing can be detrimental, because the discontinuity at token boundaries reduces the local context available near the token edges and decreases inductive bias.
Ranked #2 on Semantic correspondence on PF-WILLOW
2 code implementations • 22 Dec 2021 • Sunghwan Hong, Seokju Cho, Jisu Nam, Seungryong Kim
We introduce a novel cost aggregation network, dubbed Volumetric Aggregation with Transformers (VAT), to tackle the few-shot segmentation task by using both convolutions and transformers to efficiently handle high dimensional correlation maps between query and support.
Ranked #3 on Semantic correspondence on PF-WILLOW