no code implementations • 10 Mar 2024 • Xinmin Qiu, Congying Han, ZiCheng Zhang, Bonan Li, Tiande Guo, Pingyu Wang, Xuecheng Nie
Developing blind video deflickering (BVD) algorithms to enhance video temporal consistency, is gaining importance amid the flourish of image processing and video generation.
no code implementations • 4 Dec 2023 • Runze He, Shaofei Huang, Xuecheng Nie, Tianrui Hui, Luoqi Liu, Jiao Dai, Jizhong Han, Guanbin Li, Si Liu
In this paper, we target the adaptive source driven 3D scene editing task by proposing a CustomNeRF model that unifies a text description or a reference image as the editing prompt.
no code implementations • 8 May 2023 • Xinmin Qiu, Congying Han, ZiCheng Zhang, Bonan Li, Tiande Guo, Xuecheng Nie
This design is implemented with two key components: 1) Identity Restoration Module (IRM) for preserving the face details in results.
no code implementations • 6 Mar 2023 • Bonan Li, ZiCheng Zhang, Xuecheng Nie, Congying Han, Yinhan Hu, Tiande Guo
And it introduces a novel triple reconstruction loss to fine-tune the pre-trained LDM for encoding style and content into corresponding identifiers; 2) Fine-grained Content Controller (FCC) for the recombination phase.
no code implementations • CVPR 2023 • Bonan Li, Yinhan Hu, Xuecheng Nie, Congying Han, Xiangjian Jiang, Tiande Guo, Luoqi Liu
Given exploration on the above three questions, we present the novel DropKey method that regards Key as the drop unit and exploits decreasing schedule for drop ratio, improving ViTs in a general way.
no code implementations • 15 Nov 2022 • Ziwen Liu, Bonan Li, Congying Han, Tiande Guo, Xuecheng Nie
In order to alleviate the discriminative information overfitting problem effectively, we employ the reconstruction task to regularize the discriminative task.
Ranked #23 on Self-Supervised Image Classification on ImageNet
Contrastive Learning Self-Supervised Image Classification +2
no code implementations • 4 Oct 2022 • Xiangjian Jiang, Xuecheng Nie, Zitian Wang, Luoqi Liu, Si Liu
Existing methods for human mesh recovery mainly focus on single-view frameworks, but they often fail to produce accurate results due to the ill-posed setup.
no code implementations • 4 Aug 2022 • Bonan Li, Yinhan Hu, Xuecheng Nie, Congying Han, Xiangjian Jiang, Tiande Guo, Luoqi Liu
Given exploration on the above three questions, we present the novel DropKey method that regards Key as the drop unit and exploits decreasing schedule for drop ratio, improving ViTs in a general way.
1 code implementation • CVPR 2022 • Zitian Wang, Xuecheng Nie, Xiaochao Qu, Yunpeng Chen, Si Liu
In this paper, we present a novel Distribution-Aware Single-stage (DAS) model for tackling the challenging multi-person 3D pose estimation problem.
3D Multi-Person Pose Estimation (absolute) 3D Multi-Person Pose Estimation (root-relative) +2
no code implementations • CVPR 2022 • Lei Jin, Chenyang Xu, Xiaojuan Wang, Yabo Xiao, Yandong Guo, Xuecheng Nie, Jian Zhao
The existing multi-person absolute 3D pose estimation methods are mainly based on two-stage paradigm, i. e., top-down or bottom-up, leading to redundant pipelines with high computation cost.
1 code implementation • CVPR 2021 • Jianfeng Zhang, Dongdong Yu, Jun Hao Liew, Xuecheng Nie, Jiashi Feng
In this work, we present a single-stage model, Body Meshes as Points (BMP), to simplify the pipeline and lift both efficiency and performance.
Ranked #9 on 3D Multi-Person Pose Estimation on MuPoTS-3D
3D Human Shape Estimation 3D Multi-Person Pose Estimation +1
no code implementations • 19 Mar 2021 • Bonan Li, Xuecheng Nie, Congying Han
In this paper, we propose to enhance the generalizability of GZSL models via improving feature diversity of unseen classes.
no code implementations • 16 Oct 2020 • Li Yuan, Shuning Chang, Ziyuan Huang, Yichen Zhou, Yunpeng Chen, Xuecheng Nie, Francis E. H. Tay, Jiashi Feng, Shuicheng Yan
This paper presents our solution to ACM MM challenge: Large-scale Human-centric Video Analysis in Complex Events\cite{lin2020human}; specifically, here we focus on Track3: Crowd Pose Tracking in Complex Events.
no code implementations • 16 Oct 2020 • Li Yuan, Shuning Chang, Xuecheng Nie, Ziyuan Huang, Yichen Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan
In this paper, we focus on improving human pose estimation in videos of crowded scenes from the perspectives of exploiting temporal context and collecting new data.
no code implementations • 16 Oct 2020 • Li Yuan, Yichen Zhou, Shuning Chang, Ziyuan Huang, Yunpeng Chen, Xuecheng Nie, Tao Wang, Jiashi Feng, Shuicheng Yan
Prior works always fail to deal with this problem in two aspects: (1) lacking utilizing information of the scenes; (2) lacking training data in the crowd and complex scenes.
no code implementations • ECCV 2020 • Chenyang Si, Xuecheng Nie, Wei Wang, Liang Wang, Tieniu Tan, Jiashi Feng
Self-supervised learning (SSL) has been proved very effective at learning representations from unlabeled data in the image domain.
no code implementations • NeurIPS 2020 • Jianfeng Zhang, Xuecheng Nie, Jiashi Feng
In this work, we propose a novel framework, Inference Stage Optimization (ISO), for improving the generalizability of 3D pose models when source and target data come from different pose distributions.
Ranked #118 on 3D Human Pose Estimation on 3DPW (PA-MPJPE metric)
1 code implementation • ICCV 2019 • Xuecheng Nie, Jianfeng Zhang, Shuicheng Yan, Jiashi Feng
Based on SPR, we develop the SPM model that can directly predict structured poses for multiple persons in a single stage, and thus offer a more compact pipeline and attractive efficiency advantage over two-stage methods.
Ranked #3 on Keypoint Detection on MPII Multi-Person
no code implementations • ICCV 2019 • Xuecheng Nie, Yuncheng Li, Linjie Luo, Ning Zhang, Jiashi Feng
Existing video-based human pose estimation methods extensively apply large networks onto every frame in the video to localize body joints, which suffer high computational cost and hardly meet the low-latency requirement in realistic applications.
Ranked #3 on 2D Human Pose Estimation on JHMDB (2D poses only)
no code implementations • ECCV 2018 • Xuecheng Nie, Jiashi Feng, Shuicheng Yan
This paper presents a novel Mutual Learning to Adapt model (MuLA) for joint human parsing and pose estimation.
Ranked #11 on Semantic Segmentation on LIP val
no code implementations • ECCV 2018 • Xuecheng Nie, Jiashi Feng, Junliang Xing, Shuicheng Yan
This paper proposes a novel Pose Partition Network (PPN) to address the challenging multi-person pose estimation problem.
no code implementations • CVPR 2018 • Xuecheng Nie, Jiashi Feng, Yiming Zuo, Shuicheng Yan
Comprehensive experiments on benchmarks LIP and extended PASCAL-Person-Part show that the proposed Parsing Induced Learner can improve performance of both single- and multi-person pose estimation to new state-of-the-art.
no code implementations • ICCV 2017 • Shengtao Xiao, Jiashi Feng, Luoqi Liu, Xuecheng Nie, Wei Wang, Shuicheng Yan, Ashraf Kassim
To address these challenging issues, we introduce a novel recurrent 3D-2D dual learning model that alternatively performs 2D-based 3D face model refinement and 3D-to-2D projection based 2D landmark refinement to reliably reason about self-occluded landmarks, precisely capture the subtle landmark displacement and accurately detect landmarks even in presence of extremely large poses.
1 code implementation • 21 May 2017 • Xuecheng Nie, Jiashi Feng, Junliang Xing, Shuicheng Yan
This paper proposes a new Generative Partition Network (GPN) to address the challenging multi-person pose estimation problem.
Ranked #1 on Multi-Person Pose Estimation on WAF (AP metric)