no code implementations • CVPR 2023 • Andreas Meuleman, Yu-Lun Liu, Chen Gao, Jia-Bin Huang, Changil Kim, Min H. Kim, Johannes Kopf
For handling unknown poses, we jointly estimate the camera poses with radiance field in a progressive manner.
no code implementations • 23 Feb 2023 • Zhixiang Wang, Yu-Lun Liu, Jia-Bin Huang, Shin'ichi Satoh, Sizhuo Ma, Gurunandan Krishnan, Jian Wang
Re-rendering the portrait at a proper focal length and camera distance effectively corrects perspective distortions and produces more natural-looking results.
no code implementations • CVPR 2023 • Yu-Lun Liu, Chen Gao, Andreas Meuleman, Hung-Yu Tseng, Ayush Saraf, Changil Kim, Yung-Yu Chuang, Johannes Kopf, Jia-Bin Huang
Dynamic radiance field reconstruction methods aim to model the time-varying structure and appearance of a dynamic scene.
1 code implementation • ICLR 2022 • Chen-Hao Chao, Wei-Fang Sun, Bo-Wun Cheng, Yi-Chen Lo, Chia-Che Chang, Yu-Lun Liu, Yu-Lin Chang, Chia-Ping Chen, Chun-Yi Lee
These methods facilitate the training procedure of conditional score models, as a mixture of scores can be separately estimated using a score model and a classifier.
1 code implementation • ICCV 2021 • Ning-Hsu Wang, Ren Wang, Yu-Lun Liu, Yu-Hao Huang, Yu-Lin Chang, Chia-Ping Chen, Kevin Jou
In this paper, we propose a method to estimate not only a depth map but an AiF image from a set of images with different focus positions (known as a focal stack).
2 code implementations • ICCV 2021 • Yu-Lun Liu, Wei-Sheng Lai, Ming-Hsuan Yang, Yung-Yu Chuang, Jia-Bin Huang
Existing video stabilization methods often generate visible distortion or require aggressive cropping of frame boundaries, resulting in smaller field of views.
no code implementations • 20 Oct 2020 • Chien-Chuan Su, Ren Wang, Hung-Jin Lin, Yu-Lun Liu, Chia-Ping Chen, Yu-Lin Chang, Soo-Chang Pei
It aims to preserve visual information of HDR images in a medium with a limited dynamic range.
1 code implementation • ECCV 2020 • Ke-Chi Chang, Ren Wang, Hung-Jin Lin, Yu-Lun Liu, Chia-Ping Chen, Yu-Lin Chang, Hwann-Tzong Chen
Modeling imaging sensor noise is a fundamental problem for image processing and computer vision applications.
1 code implementation • 11 Aug 2020 • Yu-Lun Liu, Wei-Sheng Lai, Ming-Hsuan Yang, Yung-Yu Chuang, Jia-Bin Huang
We present a learning-based approach for removing unwanted obstructions, such as window reflections, fence occlusions, or adherent raindrops, from a short sequence of images captured by a moving camera.
1 code implementation • CVPR 2020 • Yu-Lun Liu, Wei-Sheng Lai, Yu-Sheng Chen, Yi-Lung Kao, Ming-Hsuan Yang, Yung-Yu Chuang, Jia-Bin Huang
We model the HDRto-LDR image formation pipeline as the (1) dynamic range clipping, (2) non-linear mapping from a camera response function, and (3) quantization.
1 code implementation • CVPR 2020 • Yu-Lun Liu, Wei-Sheng Lai, Ming-Hsuan Yang, Yung-Yu Chuang, Jia-Bin Huang
We present a learning-based approach for removing unwanted obstructions, such as window reflections, fence occlusions or raindrops, from a short sequence of images captured by a moving camera.
1 code implementation • AAAI 2020 : The Thirty-Fourth AAAI Conference on Artificial Intelligence 2020 • Yu-Ju Tsai, Yu-Lun Liu, Ming Ouhyoung, Yung-Yu Chuang
This paper introduces a novel deep network for estimating depth maps from a light field image.
Ranked #1 on
Depth Estimation
on 4D Light Field Dataset
1 code implementation • AAAI 2019 • Yu-Lun Liu, Yi-Tung Liao, Yen-Yu Lin, Yung-Yu Chuang1, 2
In addition to the cycle consistency loss, we propose two extensions: motion linearity loss and edge-guided training.