no code implementations • 11 Apr 2023 • Soohyun Kim, Junho Kim, Taekyung Kim, Hwan Heo, Seungryong Kim, Jiyoung Lee, Jin-Hwa Kim
This task is difficult due to the geometric distortion of panoramic images and the lack of a panoramic image dataset with diverse conditions, like weather or time.
no code implementations • 3 Feb 2023 • Hwan Heo, Taekyung Kim, Jiyoung Lee, Jaewon Lee, Soohyun Kim, Hyunwoo J. Kim, Jin-Hwa Kim
Multi-resolution hash encoding has recently been proposed to reduce the computational cost of neural renderings, such as NeRF.
1 code implementation • CVPR 2023 • JiHye Park, Sunwoo Kim, Soohyun Kim, Seokju Cho, Jaejun Yoo, Youngjung Uh, Seungryong Kim
Existing techniques for image-to-image translation commonly have suffered from two critical problems: heavy reliance on per-sample domain annotation and/or inability of handling multiple attributes per image.
1 code implementation • CVPR 2022 • Soohyun Kim, Jongbeom Baek, JiHye Park, Gyeongnyeon Kim, Seungryong Kim
By augmenting such tokens with an instance-level feature extracted from the content feature with respect to bounding box information, our framework is capable of learning an interaction between object instances and the global image, thus boosting the instance-awareness.
1 code implementation • 12 Dec 2021 • Sunwoo Kim, Soohyun Kim, Seungryong Kim
Recent techniques to solve photorealistic style transfer within deep convolutional neural networks (CNNs) generally require intensive training from large-scale datasets, thus having limited applicability and poor generalization ability to unseen images or styles.
1 code implementation • 12 Aug 2021 • Antyanta Bangunharcana, Jae Won Cho, Seokju Lee, In So Kweon, Kyung-Soo Kim, Soohyun Kim
Volumetric deep learning approach towards stereo matching aggregates a cost volume computed from input left and right images using 3D convolutions.
no code implementations • 18 Nov 2020 • Taewon Kang, Soohyun Kim, Sunwoo Kim, Seungryong Kim
Existing techniques to solve exemplar-based image-to-image translation within deep convolutional neural networks (CNNs) generally require a training phase to optimize the network parameters on domain-specific and task-specific benchmarks, thus having limited applicability and generalization ability.