no code implementations • 23 Jul 2022 • Wei-Sheng Lai, YiChang Shih, Lun-Cheng Chu, Xiaotong Wu, Sung-Fang Tsai, Michael Krainin, Deqing Sun, Chia-Kai Liang
To the best of our knowledge, our work is the first mobile solution for face motion deblurring that works reliably and robustly over thousands of images in diverse motion and lighting conditions.
1 code implementation • 12 Jul 2022 • Kai-En Lin, Lin Yen-Chen, Wei-Sheng Lai, Tsung-Yi Lin, Yi-Chang Shih, Ravi Ramamoorthi
Existing approaches condition on local image features to reconstruct a 3D object, but often render blurry predictions at viewpoints that are far away from the source view.
no code implementations • 26 Jan 2022 • Kaihao Zhang, Wenqi Ren, Wenhan Luo, Wei-Sheng Lai, Bjorn Stenger, Ming-Hsuan Yang, Hongdong Li
Image deblurring is a classic problem in low-level computer vision with the aim to recover a sharp image from a blurred input image.
no code implementations • 18 Nov 2021 • Wei-Sheng Lai, YiChang Shih, Chia-Kai Liang, Ming-Hsuan Yang
Video blogs and selfies are popular social media formats, which are often captured by wide-angle cameras to show human subjects and expanded background.
no code implementations • 8 Sep 2021 • Sanghyun Son, Jaeha Kim, Wei-Sheng Lai, Ming-Husan Yang, Kyoung Mu Lee
Most image super-resolution (SR) methods are developed on synthetic low-resolution (LR) and high-resolution (HR) image pairs that are constructed by a predetermined operation, e. g., bicubic downsampling.
no code implementations • 27 May 2021 • Pei-Ze Chiang, Meng-Shiun Tsai, Hung-Yu Tseng, Wei-Sheng Lai, Wei-Chen Chiu
Our framework consists of two components: an implicit representation of the 3D scene with the neural radiance fields model, and a hypernetwork to transfer the style information into the scene representation.
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.
1 code implementation • 2 Feb 2021 • Zhenmei Shi, Fuhao Shi, Wei-Sheng Lai, Chia-Kai Liang, YIngyu Liang
We present a deep neural network (DNN) that uses both sensor data (gyroscope) and image content (optical flow) to stabilize videos through unsupervised learning.
1 code implementation • Winter Conference on Applications of Computer Vision (WACV) 2021 • Min-Yuan Tseng, Yen-Chung Chen, Yi-Lun Lee, Wei-Sheng Lai, Yi-Hsuan Tsai, Wei-Chen Chiu
Our method is based on an important observation that: even the direct cascade of prior research in spatial and temporal super-resolution can achieve the spatiotemporal upsampling, changing orders for combining them would lead to results with a complementary property.
no code implementations • 10 Dec 2020 • Chen Gao, YiChang Shih, Wei-Sheng Lai, Chia-Kai Liang, Jia-Bin Huang
We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait.
no code implementations • 20 Oct 2020 • Xide Xia, Tianfan Xue, Wei-Sheng Lai, Zheng Sun, Abby Chang, Brian Kulis, Jiawen Chen
We present a novel algorithm for transferring artistic styles of semantically meaningful local regions of an image onto local regions of a target video while preserving its photorealism.
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, 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 • 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 • 2 Mar 2020 • Xinyi Zhang, Hang Dong, Zhe Hu, Wei-Sheng Lai, Fei Wang, Ming-Hsuan Yang
To address this problem, we propose a dual-branch convolutional neural network to extract base features and recovered features separately.
no code implementations • 19 Jan 2020 • Ziyi Shen, Wei-Sheng Lai, Tingfa Xu, Jan Kautz, Ming-Hsuan Yang
Specifically, we first use a coarse deblurring network to reduce the motion blur on the input face image.
no code implementations • 10 Jan 2020 • Shih-Han Chou, Wei-Lun Chao, Wei-Sheng Lai, Min Sun, Ming-Hsuan Yang
We then study two different VQA models on VQA 360, including one conventional model that takes an equirectangular image (with intrinsic distortion) as input and one dedicated model that first projects a 360 image onto cubemaps and subsequently aggregates the information from multiple spatial resolutions.
no code implementations • 31 Jul 2019 • Wei-Sheng Lai, Orazio Gallo, Jinwei Gu, Deqing Sun, Ming-Hsuan Yang, Jan Kautz
Despite the long history of image and video stitching research, existing academic and commercial solutions still produce strong artifacts.
5 code implementations • CVPR 2019 • Wenbo Bao, Wei-Sheng Lai, Chao Ma, Xiaoyun Zhang, Zhiyong Gao, Ming-Hsuan Yang
The proposed model then warps the input frames, depth maps, and contextual features based on the optical flow and local interpolation kernels for synthesizing the output frame.
Ranked #5 on Video Frame Interpolation on Middlebury
1 code implementation • 20 Oct 2018 • Wenbo Bao, Wei-Sheng Lai, Xiaoyun Zhang, Zhiyong Gao, Ming-Hsuan Yang
Recently, a number of data-driven frame interpolation methods based on convolutional neural networks have been proposed.
Ranked #21 on Video Frame Interpolation on Vimeo90K
1 code implementation • arXiv 2018 • Wenbo Bao, Wei-Sheng Lai, Xiaoyun Zhang, Zhiyong Gao, Ming-Hsuan Yang
In this work, we propose a motion estimation and motion compensation driven neural network for video frame interpolation.
Ranked #6 on Video Frame Interpolation on Middlebury
1 code implementation • ECCV 2018 • Wei-Sheng Lai, Jia-Bin Huang, Oliver Wang, Eli Shechtman, Ersin Yumer, Ming-Hsuan Yang
Our method takes the original unprocessed and per-frame processed videos as inputs to produce a temporally consistent video.
2 code implementations • 27 Jul 2018 • Xinyi Zhang, Hang Dong, Zhe Hu, Wei-Sheng Lai, Fei Wang, Ming-Hsuan Yang
Single-image super-resolution is a fundamental task for vision applications to enhance the image quality with respect to spatial resolution.
no code implementations • CVPR 2018 • Lerenhan Li, Jinshan Pan, Wei-Sheng Lai, Changxin Gao, Nong Sang, Ming-Hsuan Yang
We present an effective blind image deblurring method based on a data-driven discriminative prior. Our work is motivated by the fact that a good image prior should favor clear images over blurred images. In this work, we formulate the image prior as a binary classifier which can be achieved by a deep convolutional neural network (CNN). The learned prior is able to distinguish whether an input image is clear or not. Embedded into the maximum a posterior (MAP) framework, it helps blind deblurring in various scenarios, including natural, face, text, and low-illumination images. However, it is difficult to optimize the deblurring method with the learned image prior as it involves a non-linear CNN. Therefore, we develop an efficient numerical approach based on the half-quadratic splitting method and gradient decent algorithm to solve the proposed model. Furthermore, the proposed model can be easily extended to non-uniform deblurring. Both qualitative and quantitative experimental results show that our method performs favorably against state-of-the-art algorithms as well as domain-specific image deblurring approaches.
no code implementations • CVPR 2018 • Ziyi Shen, Wei-Sheng Lai, Tingfa Xu, Jan Kautz, Ming-Hsuan Yang
In this paper, we present an effective and efficient face deblurring algorithm by exploiting semantic cues via deep convolutional neural networks (CNNs).
no code implementations • NeurIPS 2017 • Wei-Sheng Lai, Jia-Bin Huang, Ming-Hsuan Yang
Convolutional neural networks (CNNs) have recently been applied to the optical flow estimation problem.
7 code implementations • 4 Oct 2017 • Wei-Sheng Lai, Jia-Bin Huang, Narendra Ahuja, Ming-Hsuan Yang
However, existing methods often require a large number of network parameters and entail heavy computational loads at runtime for generating high-accuracy super-resolution results.
1 code implementation • CVPR 2017 • Wei-Sheng Lai, Jia-Bin Huang, Narendra Ahuja, Ming-Hsuan Yang
Convolutional neural networks have recently demonstrated high-quality reconstruction for single-image super-resolution.
Ranked #36 on Image Super-Resolution on Urban100 - 4x upscaling
no code implementations • 31 Mar 2017 • Wei-Sheng Lai, Yujia Huang, Neel Joshi, Chris Buehler, Ming-Hsuan Yang, Sing Bing Kang
We present a system for converting a fully panoramic ($360^\circ$) video into a normal field-of-view (NFOV) hyperlapse for an optimal viewing experience.
no code implementations • CVPR 2017 • Jiawei Zhang, Jinshan Pan, Wei-Sheng Lai, Rynson Lau, Ming-Hsuan Yang
In this paper, we propose a fully convolutional networks for iterative non-blind deconvolution We decompose the non-blind deconvolution problem into image denoising and image deconvolution.
no code implementations • CVPR 2016 • Wei-Sheng Lai, Jia-Bin Huang, Zhe Hu, Narendra Ahuja, Ming-Hsuan Yang
Using these datasets, we conduct a large-scale user study to quantify the performance of several representative state-of-the-art blind deblurring algorithms.
no code implementations • CVPR 2015 • Wei-Sheng Lai, Jian-Jiun Ding, Yen-Yu Lin, Yung-Yu Chuang
The intermediate patches can then guide the estimation of the blur kernel.