activity
20172023
most citedCross-Domain Style Mixing for Face Cartoonization

5 citations · 20 across the 10 of their papers we have counts for

collaborators

10 papers

cs.CV2023★ 5 cited

AesPA-Net: Aesthetic Pattern-Aware Style Transfer Networks

Kibeom Hong, Seogkyu Jeon, Junsoo Lee +6

To deliver the artistic expression of the target style, recent studies exploit the attention mechanism owing to its ability to map the local patches of the style image to the corre…

cs.CV2023

Chupa: Carving 3D Clothed Humans from Skinned Shape Priors using 2D Diffusion Probabilistic Models

Byungjun Kim, Patrick Kwon, Kwangho Lee +4

We propose a 3D generation pipeline that uses diffusion models to generate realistic human digital avatars. Due to the wide variety of human identities, poses, and stochastic detai…

cs.CV2022

Guiding Users to Where to Give Color Hints for Efficient Interactive Sketch Colorization via Unsupervised Region Prioritization

Youngin Cho, Junsoo Lee, Soyoung Yang +6

Existing deep interactive colorization models have focused on ways to utilize various types of interactions, such as point-wise color hints, scribbles, or natural-language texts, a…

cs.CV2022★ 5 cited

Cross-Domain Style Mixing for Face Cartoonization

Seungkwon Kim, Chaeheon Gwak, Dohyun Kim +4

Cartoon domain has recently gained increasing popularity. Previous studies have attempted quality portrait stylization into the cartoon domain; however, this poses a great challeng…

cs.CV2021

Maximizing Cosine Similarity Between Spatial Features for Unsupervised Domain Adaptation in Semantic Segmentation

Inseop Chung, Daesik Kim, Nojun Kwak

We propose a novel method that tackles the problem of unsupervised domain adaptation for semantic segmentation by maximizing the cosine similarity between the source and the target…

cs.CV2019★ 3 cited

Tell Me What They're Holding: Weakly-supervised Object Detection with Transferable Knowledge from Human-object Interaction

Daesik Kim, Gyujeong Lee, Jisoo Jeong +1

In this work, we introduce a novel weakly supervised object detection (WSOD) paradigm to detect objects belonging to rare classes that have not many examples using transferable kno…