no code implementations • 19 Apr 2024 • Yixiang Zhuang, Baoping Cheng, Yao Cheng, Yuntao Jin, Renshuai Liu, Chengyang Li, Xuan Cheng, Jing Liao, Juncong Lin
Speech-driven facial animation methods usually contain two main classes, 3D and 2D talking face, both of which attract considerable research attention in recent years.
no code implementations • 2 Jan 2024 • Renshuai Liu, Bowen Ma, Wei zhang, Zhipeng Hu, Changjie Fan, Tangjie Lv, Yu Ding, Xuan Cheng
We devise a novel diffusion model that can undertake the task of simultaneously face swapping and reenactment.
1 code implementation • 22 May 2023 • Renshuai Liu, Chengyang Li, Haitao Cao, Yinglin Zheng, Ming Zeng, Xuan Cheng
In the second stage, we tune the imitator network by optimizing the style code, in order to find an optimal fusion result for each input pair.
1 code implementation • 22 Jun 2022 • Yiwei Ding, Wenjin Deng, Yinglin Zheng, PengFei Liu, Meihong Wang, Xuan Cheng, Jianmin Bao, Dong Chen, Ming Zeng
In this paper, we present the Intra- and Inter-Human Relation Networks (I^2R-Net) for Multi-Person Pose Estimation.
Ranked #2 on Multi-Person Pose Estimation on OCHuman
no code implementations • 22 Mar 2022 • Shixiao Fan, Xuan Cheng, Xiaomin Wang, Chun Yang, Pan Deng, Minghui Liu, Jiali Deng, Ming Liu
Recently, researchers have shown an increased interest in the online knowledge distillation.
no code implementations • 2 Dec 2021 • Tianshu Xie, Xuan Cheng, Minghui Liu, Jiali Deng, Xiaomin Wang, Ming Liu
In this paper, we observe that the reduced image retains relatively complete shape semantics but loses extensive texture information.
no code implementations • 18 Jul 2021 • Tianshu Xie, Xuan Cheng, Xiaomin Wang, Minghui Liu, Jiali Deng, Ming Liu
In this paper, we propose a novel training strategy for convolutional neural network(CNN) named Feature Mining, that aims to strengthen the network's learning of the local feature.
no code implementations • 12 Jun 2021 • Xuan Cheng, Tianshu Xie, Xiaomin Wang, Jiali Deng, Minghui Liu, Ming Liu
Regularization and data augmentation methods have been widely used and become increasingly indispensable in deep learning training.
no code implementations • 9 Jun 2021 • Zilin Ding, Yuhang Yang, Xuan Cheng, Xiaomin Wang, Ming Liu
In this paper we find that features in CNNs can be also used for self-supervision.
no code implementations • 9 Jun 2021 • Yuhang Yang, Zilin Ding, Xuan Cheng, Xiaomin Wang, Ming Liu
In this paper, we show that feature transformations within CNNs can also be regarded as supervisory signals to construct the self-supervised task, called \emph{internal pretext task}.
no code implementations • 8 Jun 2021 • Xuan Cheng, Tianshu Xie, Xiaomin Wang, Jiali Deng, Minghui Liu, Ming Liu
The promising performances of CNNs often overshadow the need to examine whether they are doing in the way we are actually interested.
no code implementations • 29 Mar 2021 • Xuan Cheng, Tianshu Xie, Xiaomin Wang, Qifeng Weng, Minghui Liu, Jiali Deng, Ming Liu
In this paper, we propose Selective Output Smoothing Regularization, a novel regularization method for training the Convolutional Neural Networks (CNNs).
no code implementations • 29 Mar 2021 • Tianshu Xie, Minghui Liu, Jiali Deng, Xuan Cheng, Xiaomin Wang, Ming Liu
In convolutional neural network (CNN), dropout cannot work well because dropped information is not entirely obscured in convolutional layers where features are correlated spatially.
1 code implementation • 9 Mar 2021 • Tianshu Xie, Xuan Cheng, Minghui Liu, Jiali Deng, Xiaomin Wang, Ming Liu
In this paper, we propose a novel data augmentation strategy named Cut-Thumbnail, that aims to improve the shape bias of the network.
no code implementations • CVPR 2013 • Ming Zeng, Jiaxiang Zheng, Xuan Cheng, Xinguo Liu
We represent the shape motion by a deformation graph, and propose a model-to-part method to gradually integrate sampled points of depth scans into the deformation graph.