no code implementations • 15 Jan 2023 • Linchao Bao, Haoxian Zhang, Yue Qian, Tangli Xue, Changhai Chen, Xuefei Zhe, Di Kang
We show that the predicted viseme curves can be applied to different viseme-rigged characters to yield various personalized animations with realistic and natural facial motions.
no code implementations • 26 Dec 2022 • Xiaodong Feng, Yue Qian, Wanfang Shen
We propose, Monte Carlo Nonlocal physics-informed neural networks (MC-Nonlocal-PINNs), which is a generalization of MC-fPINNs in \cite{guo2022monte}, for solving general nonlocal models such as integral equations and nonlocal PDEs.
1 code implementation • 17 Dec 2022 • Qijian Zhang, Junhui Hou, Yue Qian, Yiming Zeng, Juyong Zhang, Ying He
In this paper, we present an unsupervised deep neural architecture called Flattening-Net to represent irregular 3D point clouds of arbitrary geometry and topology as a completely regular 2D point geometry image (PGI) structure, in which coordinates of spatial points are captured in colors of image pixels.
1 code implementation • 7 Jul 2022 • Qijian Zhang, Junhui Hou, Yue Qian
In this paper, we explore the possibility of boosting deep 3D point cloud encoders by transferring visual knowledge extracted from deep 2D image encoders under a standard teacher-student distillation workflow.
no code implementations • 14 Jun 2022 • Runsong Zhu, Di Kang, Ka-Hei Hui, Yue Qian, Xuefei Zhe, Zhen Dong, Linchao Bao, Pheng-Ann Heng, Chi-Wing Fu
To guide the network quickly fit the coarse shape, we propose to utilize the signed supervision in regions that are obviously outside the object and can be easily determined, resulting in our semi-signed supervision.
1 code implementation • CVPR 2022 • Yingzhi Tang, Yue Qian, Qijian Zhang, Yiming Zeng, Junhui Hou, Xuefei Zhe
We propose WarpingGAN, an effective and efficient 3D point cloud generation network.
1 code implementation • CVPR 2022 • Yiming Zeng, Yue Qian, Qijian Zhang, Junhui Hou, Yixuan Yuan, Ying He
This paper investigates the problem of temporally interpolating dynamic 3D point clouds with large non-rigid deformation.
1 code implementation • CVPR 2021 • Yiming Zeng, Yue Qian, Zhiyu Zhu, Junhui Hou, Hui Yuan, Ying He
The symmetric deformer, with an additional regularized loss, transforms the two permuted point clouds to each other to drive the unsupervised learning of the correspondence.
Ranked #6 on 3D Dense Shape Correspondence on SHREC'19 (using extra training data)
no code implementations • 5 Dec 2020 • Qijian Zhang, Junhui Hou, Yue Qian, Juyong Zhang, Ying He
Although convolutional neural networks have achieved remarkable success in analyzing 2D images/videos, it is still non-trivial to apply the well-developed 2D techniques in regular domains to the irregular 3D point cloud data.
1 code implementation • 25 Nov 2020 • Yue Qian, Junhui Hou, Sam Kwong, Ying He
In addition, we propose a simple yet effective training strategy to drive such a flexible ability.
1 code implementation • 1 May 2020 • Yue Qian, Junhui Hou, Qijian Zhang, Yiming Zeng, Sam Kwong, Ying He
This paper explores the problem of task-oriented downsampling over 3D point clouds, which aims to downsample a point cloud while maintaining the performance of subsequent applications applied to the downsampled sparse points as much as possible.
1 code implementation • ECCV 2020 • Yue Qian, Junhui Hou, Sam Kwong, Ying He
Matrix $\mathbf T$ approximates the augmented Jacobian matrix of a local parameterization and builds a one-to-one correspondence between the 2D parametric domain and the 3D tangent plane so that we can lift the adaptively distributed 2D samples (which are also learned from data) to 3D space.
no code implementations • 20 Jun 2019 • Chongyi Li, Runmin Cong, Junhui Hou, Sanyi Zhang, Yue Qian, Sam Kwong
Arising from the various object types and scales, diverse imaging orientations, and cluttered backgrounds in optical remote sensing image (RSI), it is difficult to directly extend the success of salient object detection for nature scene image to the optical RSI.