Search Results for author: Xuecheng Nie

Found 16 papers, 3 papers with code

Distribution-Aware Single-Stage Models for Multi-Person 3D Pose Estimation

no code implementations 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 Pose Estimation

Single-Stage Is Enough: Multi-Person Absolute 3D Pose Estimation

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.

3D Pose Estimation Depth Estimation

Body Meshes as Points

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.

3D Human Shape Estimation 3D Multi-Person Pose Estimation +1

DFS: A Diverse Feature Synthesis Model for Generalized Zero-Shot Learning

no code implementations19 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.

Generalized Zero-Shot Learning

A Simple Baseline for Pose Tracking in Videos of Crowded Scenes

no code implementations16 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.

Multi-Object Tracking Optical Flow Estimation +1

Towards Accurate Human Pose Estimation in Videos of Crowded Scenes

no code implementations16 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.

Optical Flow Estimation Pose Estimation

Toward Accurate Person-level Action Recognition in Videos of Crowded Scenes

no code implementations16 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.

Action Recognition Action Recognition In Videos +2

Inference Stage Optimization for Cross-scenario 3D Human Pose Estimation

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.

3D Human Pose Estimation Self-Supervised Learning

Dynamic Kernel Distillation for Efficient Pose Estimation in Videos

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.

Pose Estimation

Single-Stage Multi-Person Pose Machines

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.

3D Pose Estimation Keypoint Detection +1

Pose Partition Networks for Multi-Person Pose Estimation

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.

Human Detection Multi-Person Pose Estimation

Human Pose Estimation With Parsing Induced Learner

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.

Human Parsing Multi-Person Pose Estimation

Recurrent 3D-2D Dual Learning for Large-Pose Facial Landmark Detection

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.

Face Model Facial Landmark Detection

Generative Partition Networks for Multi-Person Pose Estimation

1 code implementation21 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)

Human Detection Keypoint Detection +1

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