1 code implementation • 7 Dec 2022 • Gyeongmin Choe, Beibei Du, Seonghyeon Nam, Xiaoyu Xiang, Bo Zhu, Rakesh Ranjan
To address this, we have developed a procedural synthetic data generation pipeline and dataset tailored to low-level vision tasks.
1 code implementation • ICCV 2021 • Dongyoung Kim, Jinwoo Kim, Seonghyeon Nam, Dongwoo Lee, Yeonkyung Lee, Nahyup Kang, Hyong-Euk Lee, ByungIn Yoo, Jae-Joon Han, Seon Joo Kim
Images in our dataset are mostly captured with illuminants existing in the scene, and the ground truth illumination is computed by taking the difference between the images with different illumination combination.
1 code implementation • NeurIPS 2019 • Yunji Kim, Seonghyeon Nam, In Cho, Seon Joo Kim
To generate future frames, we first detect keypoints of a moving object and predict future motion as a sequence of keypoints.
1 code implementation • NeruIPS 2022 • Sejong Yang, Subin Jeon, Seonghyeon Nam, Seon Joo Kim
There are three main obstacles for interspecies face understanding: (1) lack of animal data compared to human, (2) ambiguous connection between faces of various animals, and (3) extreme shape and style variance.
1 code implementation • CVPR 2022 • Seonghyeon Nam, Abhijith Punnappurath, Marcus A. Brubaker, Michael S. Brown
Our experiments show that our learned sampling can adapt to the image content to produce better raw reconstructions than existing methods.
1 code implementation • 16 Apr 2021 • Young Hwi Kim, Seonghyeon Nam, Seon Joo Kim
Many video understanding tasks work in the offline setting by assuming that the input video is given from the start to the end.
no code implementations • ICCV 2017 • Seonghyeon Nam, Seon Joo Kim
Often called as the radiometric calibration, the process of recovering RAW images from processed images (JPEG format in the sRGB color space) is essential for many computer vision tasks that rely on physically accurate radiance values.
no code implementations • 26 Jun 2017 • Seonghyeon Nam, Seon Joo Kim
Also, spatially varying photo adjustment methods have been studied by exploiting high-level features and semantic label maps.
no code implementations • NeurIPS 2018 • Seonghyeon Nam, Yunji Kim, Seon Joo Kim
Our task aims to semantically modify visual attributes of an object in an image according to the text describing the new visual appearance.
no code implementations • CVPR 2016 • Seonghyeon Nam, Youngbae Hwang, Yasuyuki Matsushita, Seon Joo Kim
Modelling and analyzing noise in images is a fundamental task in many computer vision systems.
no code implementations • CVPR 2019 • Seonghyeon Nam, Chongyang Ma, Menglei Chai, William Brendel, Ning Xu, Seon Joo Kim
Time-lapse videos usually contain visually appealing content but are often difficult and costly to create.
no code implementations • 20 Mar 2020 • Younghyun Jo, Jaeyeon Kang, Seoung Wug Oh, Seonghyeon Nam, Peter Vajda, Seon Joo Kim
Our framework is similar to GANs in that we iteratively train two networks - a generator and a loss network.
no code implementations • ECCV 2020 • Subin Jeon, Seonghyeon Nam, Seoung Wug Oh, Seon Joo Kim
To reduce the training-testing discrepancy of the self-supervised learning, a novel cross-identity training scheme is additionally introduced.
no code implementations • 2 Aug 2021 • Seonghyeon Nam, Marcus A. Brubaker, Michael S. Brown
We propose a framework for aligning and fusing multiple images into a single view using neural image representations (NIRs), also known as implicit or coordinate-based neural representations.
no code implementations • CVPR 2023 • Ziyu Wan, Christian Richardt, Aljaž Božič, Chao Li, Vijay Rengarajan, Seonghyeon Nam, Xiaoyu Xiang, Tuotuo Li, Bo Zhu, Rakesh Ranjan, Jing Liao
Neural radiance fields (NeRFs) enable novel view synthesis with unprecedented visual quality.
no code implementations • 1 Feb 2024 • Hyunyoung Jung, Seonghyeon Nam, Nikolaos Sarafianos, Sungjoo Yoo, Alexander Sorkine-Hornung, Rakesh Ranjan
Shape and geometric patterns are essential in defining stylistic identity.