no code implementations • ECCV 2020 • Xin Xiong, Haipeng Xiong, Ke Xian, Chen Zhao, Zhiguo Cao, Xin Li
Depth completion is a widely studied problem of predicting a dense depth map from a sparse set of measurements and a single RGB image.
no code implementations • 15 Mar 2024 • Huiqiang Sun, Xingyi Li, Liao Shen, Xinyi Ye, Ke Xian, Zhiguo Cao
Experimental results on our dataset demonstrate that our method outperforms existing approaches in generating sharp novel views from motion-blurred inputs while maintaining spatial-temporal consistency of the scene.
no code implementations • 10 Mar 2024 • Xingyi Li, Zhiguo Cao, Yizheng Wu, Kewei Wang, Ke Xian, Zhe Wang, Guosheng Lin
To address this limitation, we present S-DyRF, a reference-based spatio-temporal stylization method for dynamic neural radiance fields.
1 code implementation • 13 Dec 2023 • Kewei Wang, Yizheng Wu, Zhiyu Pan, Xingyi Li, Ke Xian, Zhe Wang, Zhiguo Cao, Guosheng Lin
To improve the quality of pseudo labels, we propose a novel motion selection and re-generation module.
no code implementations • 20 Aug 2023 • Liao Shen, Xingyi Li, Huiqiang Sun, Juewen Peng, Ke Xian, Zhiguo Cao, Guosheng Lin
To animate the visual content, the feature point cloud is displaced based on the scene flow derived from motion estimation and the corresponding camera pose.
no code implementations • 4 Aug 2023 • Jiaqi Li, Yiran Wang, Zihao Huang, Jinghong Zheng, Ke Xian, Zhiguo Cao, Jianming Zhang
We leverage the structural characteristics of diffusion model to enforce depth structures of depth models in a plug-and-play manner.
3 code implementations • ICCV 2023 • Yiran Wang, Min Shi, Jiaqi Li, Zihao Huang, Zhiguo Cao, Jianming Zhang, Ke Xian, Guosheng Lin
Video depth estimation aims to infer temporally consistent depth.
Ranked #16 on Monocular Depth Estimation on NYU-Depth V2 (using extra training data)
no code implementations • 7 Jun 2023 • Xianrui Luo, Juewen Peng, Ke Xian, Zijin Wu, Zhiguo Cao
To this end, we present a Defocus to Focus (D2F) framework to learn realistic bokeh rendering by fusing defocus priors with the all-in-focus image and by implementing radiance priors in layered fusion.
1 code implementation • CVPRW 2023 • Marcos V. Conde, Manuel Kolmet, Tim Seizinger, Tom E. Bishop, Radu Timofte, Xiangyu Kong, Dafeng Zhang, Jinlong Wu, Fan Wang, Juewen Peng, Zhiyu Pan, Chengxin Liu, Xianrui Luo, Huiqiang Sun, Liao Shen, Zhiguo Cao, Ke Xian, Chaowei Liu, Zigeng Chen, Xingyi Yang, Songhua Liu, Yongcheng Jing, Michael Bi Mi, Xinchao Wang, Zhihao Yang, Wenyi Lian, Siyuan Lai, Haichuan Zhang, Trung Hoang, Amirsaeed Yazdani, Vishal Monga, Ziwei Luo, Fredrik K. Gustafsson, Zheng Zhao, Jens Sjölund, Thomas B. Schön, Yuxuan Zhao, Baoliang Chen, Yiqing Xu, JiXiang Niu
We present the new Bokeh Effect Transformation Dataset (BETD), and review the proposed solutions for this novel task at the NTIRE 2023 Bokeh Effect Transformation Challenge.
no code implementations • 11 Apr 2023 • Xianrui Luo, Juewen Peng, Weiyue Zhao, Ke Xian, Hao Lu, Zhiguo Cao
Benefiting from the multi-camera module in modern smartphones, we introduce a new task of AIF synthesis from main (wide) and ultra-wide cameras.
no code implementations • CVPR 2023 • Xingyi Li, Zhiguo Cao, Huiqiang Sun, Jianming Zhang, Ke Xian, Guosheng Lin
To animate the scene, we perform motion estimation and lift the 2D motion into the 3D scene flow.
1 code implementation • 29 Sep 2022 • Xingyi Li, Chaoyi Hong, Yiran Wang, Zhiguo Cao, Ke Xian, Guosheng Lin
We study the problem of novel view synthesis of objects from a single image.
1 code implementation • 31 Jul 2022 • Yiran Wang, Zhiyu Pan, Xingyi Li, Zhiguo Cao, Ke Xian, Jianming Zhang
Temporal consistency is the key challenge of video depth estimation.
1 code implementation • 18 Jul 2022 • Juewen Peng, Jianming Zhang, Xianrui Luo, Hao Lu, Ke Xian, Zhiguo Cao
Partial occlusion effects are a phenomenon that blurry objects near a camera are semi-transparent, resulting in partial appearance of occluded background.
1 code implementation • CVPR 2022 • Juewen Peng, Zhiguo Cao, Xianrui Luo, Hao Lu, Ke Xian, Jianming Zhang
Based on this formulation, we implement the classical renderer by a scattering-based method and propose a two-stage neural renderer to fix the erroneous areas from the classical renderer.
1 code implementation • CVPR 2021 • Chaoyi Hong, Shuaiyuan Du, Ke Xian, Hao Lu, Zhiguo Cao, Weicai Zhong
To this end, we introduce the concept of the key composition map (KCM) to encode the composition rules.
Ranked #1 on Image Cropping on FLMS
no code implementations • 17 May 2021 • Andrey Ignatov, Grigory Malivenko, David Plowman, Samarth Shukla, Radu Timofte, Ziyu Zhang, Yicheng Wang, Zilong Huang, Guozhong Luo, Gang Yu, Bin Fu, Yiran Wang, Xingyi Li, Min Shi, Ke Xian, Zhiguo Cao, Jin-Hua Du, Pei-Lin Wu, Chao Ge, Jiaoyang Yao, Fangwen Tu, Bo Li, Jung Eun Yoo, Kwanggyoon Seo, Jialei Xu, Zhenyu Li, Xianming Liu, Junjun Jiang, Wei-Chi Chen, Shayan Joya, Huanhuan Fan, Zhaobing Kang, Ang Li, Tianpeng Feng, Yang Liu, Chuannan Sheng, Jian Yin, Fausto T. Benavide
While many solutions have been proposed for this task, they are usually very computationally expensive and thus are not applicable for on-device inference.
no code implementations • 10 Nov 2020 • Andrey Ignatov, Radu Timofte, Ming Qian, Congyu Qiao, Jiamin Lin, Zhenyu Guo, Chenghua Li, Cong Leng, Jian Cheng, Juewen Peng, Xianrui Luo, Ke Xian, Zijin Wu, Zhiguo Cao, Densen Puthussery, Jiji C V, Hrishikesh P S, Melvin Kuriakose, Saikat Dutta, Sourya Dipta Das, Nisarg A. Shah, Kuldeep Purohit, Praveen Kandula, Maitreya Suin, A. N. Rajagopalan, Saagara M B, Minnu A L, Sanjana A R, Praseeda S, Ge Wu, Xueqin Chen, Tengyao Wang, Max Zheng, Hulk Wong, Jay Zou
This paper reviews the second AIM realistic bokeh effect rendering challenge and provides the description of the proposed solutions and results.
1 code implementation • CVPR 2020 • Ke Xian, Jianming Zhang, Oliver Wang, Long Mai, Zhe Lin, Zhiguo Cao
Large-scale disparity data generated from stereo photos and 3D videos is a promising source of supervision, however, such disparity data can only approximate the inverse ground truth depth up to an affine transformation.
no code implementations • 3 Sep 2019 • Chen Zhao, Jiaqi Yang, Ke Xian, Zhiguo Cao, Xin Li
Matching corresponding features between two images is a fundamental task to computer vision with numerous applications in object recognition, robotics, and 3D reconstruction.
no code implementations • 5 Jul 2019 • Jiaqi Yang, Ke Xian, Peng Wang, Yanning Zhang
Seeking consistent point-to-point correspondences between 3D rigid data (point clouds, meshes, or depth maps) is a fundamental problem in 3D computer vision.
no code implementations • 27 Apr 2019 • Jiaqi Yang, Chen Zhao, Ke Xian, Angfan Zhu, Zhiguo Cao
This paper presents a simple yet very effective data-driven approach to fuse both low-level and high-level local geometric features for 3D rigid data matching.
1 code implementation • 11 Jul 2018 • Ruibo Li, Ke Xian, Chunhua Shen, Zhiguo Cao, Hao Lu, Lingxiao Hang
However, robust depth prediction suffers from two challenging problems: a) How to extract more discriminative features for different scenes (compared to a single scene)?
no code implementations • 2 Jun 2018 • Yuanzhouhan Cao, Tianqi Zhao, Ke Xian, Chunhua Shen, Zhiguo Cao, Shugong Xu
In this paper, we propose to improve the performance of metric depth estimation with relative depths collected from stereo movie videos using existing stereo matching algorithm.
no code implementations • CVPR 2018 • Ke Xian, Chunhua Shen, Zhiguo Cao, Hao Lu, Yang Xiao, Ruibo Li, Zhenbo Luo
In this paper we study the problem of monocular relative depth perception in the wild.
no code implementations • 6 Apr 2018 • Jiaqi Yang, Ke Xian, Yang Xiao, Zhiguo Cao
This paper presents a thorough evaluation of several widely-used 3D correspondence grouping algorithms, motived by their significance in vision tasks relying on correct feature correspondences.
1 code implementation • ICCV 2017 • Hao Lu, Lei Zhang, Zhiguo Cao, Wei Wei, Ke Xian, Chunhua Shen, Anton Van Den Hengel
Domain adaption (DA) allows machine learning methods trained on data sampled from one distribution to be applied to data sampled from another.