42 citations · 173 across the 14 of their papers we have counts for
14 papers
COIN: Control-Inpainting Diffusion Prior for Human and Camera Motion Estimation
Jiefeng Li, Ye Yuan, Davis Rempe +5
Estimating global human motion from moving cameras is challenging due to the entanglement of human and camera motions. To mitigate the ambiguity, existing methods leverage learned…
ShapeBoost: Boosting Human Shape Estimation with Part-Based Parameterization and Clothing-Preserving Augmentation
Siyuan Bian, Jiefeng Li, Jiasheng Tang +1
Accurate human shape recovery from a monocular RGB image is a challenging task because humans come in different shapes and sizes and wear different clothes. In this paper, we propo…
NIKI: Neural Inverse Kinematics with Invertible Neural Networks for 3D Human Pose and Shape Estimation
Jiefeng Li, Siyuan Bian, Qi Liu +3
With the progress of 3D human pose and shape estimation, state-of-the-art methods can either be robust to occlusions or obtain pixel-aligned accuracy in non-occlusion cases. Howeve…
HybrIK-X: Hybrid Analytical-Neural Inverse Kinematics for Whole-body Mesh Recovery
Jiefeng Li, Siyuan Bian, Chao Xu +3
Recovering whole-body mesh by inferring the abstract pose and shape parameters from visual content can obtain 3D bodies with realistic structures. However, the inferring process is…
AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Time
Hao-Shu Fang, Jiefeng Li, Hongyang Tang +5
Accurate whole-body multi-person pose estimation and tracking is an important yet challenging topic in computer vision. To capture the subtle actions of humans for complex behavior…
D&D: Learning Human Dynamics from Dynamic Camera
Jiefeng Li, Siyuan Bian, Chao Xu +3
3D human pose estimation from a monocular video has recently seen significant improvements. However, most state-of-the-art methods are kinematics-based, which are prone to physical…
Constructing Balance from Imbalance for Long-tailed Image Recognition
Yue Xu, Yong-Lu Li, Jiefeng Li +1
Long-tailed image recognition presents massive challenges to deep learning systems since the imbalance between majority (head) classes and minority (tail) classes severely skews th…
TDAF: Top-Down Attention Framework for Vision Tasks
Bo Pang, Yizhuo Li, Jiefeng Li +3
Human attention mechanisms often work in a top-down manner, yet it is not well explored in vision research. Here, we propose the Top-Down Attention Framework (TDAF) to capture top-…
CPF: Learning a Contact Potential Field to Model the Hand-Object Interaction
Lixin Yang, Xinyu Zhan, Kailin Li +3
Modeling the hand-object (HO) interaction not only requires estimation of the HO pose, but also pays attention to the contact due to their interaction. Significant progress has bee…
HybrIK: A Hybrid Analytical-Neural Inverse Kinematics Solution for 3D Human Pose and Shape Estimation
Jiefeng Li, Chao Xu, Zhicun Chen +3
Model-based 3D pose and shape estimation methods reconstruct a full 3D mesh for the human body by estimating several parameters. However, learning the abstract parameters is a high…
HMOR: Hierarchical Multi-Person Ordinal Relations for Monocular Multi-Person 3D Pose Estimation
Jiefeng Li, Can Wang, Wentao Liu +2
Remarkable progress has been made in 3D human pose estimation from a monocular RGB camera. However, only a few studies explored 3D multi-person cases. In this paper, we attempt to…
Detailed 2D-3D Joint Representation for Human-Object Interaction
Yong-Lu Li, Xinpeng Liu, Han Lu +4
Human-Object Interaction (HOI) detection lies at the core of action understanding. Besides 2D information such as human/object appearance and locations, 3D pose is also usually uti…
CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark
Jiefeng Li, Can Wang, Hao Zhu +3
Multi-person pose estimation is fundamental to many computer vision tasks and has made significant progress in recent years. However, few previous methods explored the problem of p…
Pose Flow: Efficient Online Pose Tracking
Yuliang Xiu, Jiefeng Li, Haoyu Wang +2
Multi-person articulated pose tracking in unconstrained videos is an important while challenging problem. In this paper, going along the road of top-down approaches, we propose a d…