Search Results for author:
Found 16 papers, 3 papers with code
Distribution-Aware Single-Stage Models for Multi-Person 3D Pose Estimation
In this paper, we present a novel Distribution-Aware Single-stage (DAS) model for tackling the challenging multi-person 3D pose estimation problem.
Single-Stage Is Enough: Multi-Person Absolute 3D Pose Estimation
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.
Body Meshes as Points
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 #4 on
3D Multi-Person Pose Estimation
on MuPoTS-3D
3D Human Shape Estimation
3D Multi-Person Pose Estimation
+1
DFS: A Diverse Feature Synthesis Model for Generalized Zero-Shot Learning
In this paper, we propose to enhance the generalizability of GZSL models via improving feature diversity of unseen classes.
A Simple Baseline for Pose Tracking in Videos of Crowded Scenes
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.
Towards Accurate Human Pose Estimation in Videos of Crowded Scenes
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.
Toward Accurate Person-level Action Recognition in Videos of Crowded Scenes
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.
Adversarial Self-Supervised Learning for Semi-Supervised 3D Action Recognition
Self-supervised learning (SSL) has been proved very effective at learning representations from unlabeled data in the image domain.
Inference Stage Optimization for Cross-scenario 3D Human Pose Estimation
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 #62 on
3D Human Pose Estimation
on 3DPW
Dynamic Kernel Distillation for Efficient Pose Estimation in Videos
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)
Single-Stage Multi-Person Pose Machines
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
Multi-Person Pose Estimation
on MPII Multi-Person
Pose Partition Networks for Multi-Person Pose Estimation
This paper proposes a novel Pose Partition Network (PPN) to address the challenging multi-person pose estimation problem.
Mutual Learning to Adapt for Joint Human Parsing and Pose Estimation
This paper presents a novel Mutual Learning to Adapt model (MuLA) for joint human parsing and pose estimation.
Ranked #8 on
Semantic Segmentation
on LIP val
Human Pose Estimation With Parsing Induced Learner
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.
Recurrent 3D-2D Dual Learning for Large-Pose Facial Landmark Detection
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.
Generative Partition Networks for Multi-Person Pose Estimation
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)
