3 code implementations • 15 Dec 2022 • Yabo Xiao, Kai Su, Xiaojuan Wang, Dongdong Yu, Lei Jin, Mingshu He, Zehuan Yuan
The existing end-to-end methods rely on dense representations to preserve the spatial detail and structure for precise keypoint localization.
1 code implementation • 8 Oct 2022 • Yabo Xiao, Xiaojuan Wang, Dongdong Yu, Kai Su, Lei Jin, Mei Song, Shuicheng Yan, Jian Zhao
With the proposed body representation, we further deliver a compact single-stage multi-person pose regression network, termed as AdaptivePose.
no code implementations • 4 Jan 2022 • Yabo Xiao, Dongdong Yu, Xiaojuan Wang, Lei Jin, Guoli Wang, Qian Zhang
Off-the-shelf single-stage multi-person pose regression methods generally leverage the instance score (i. e., confidence of the instance localization) to indicate the pose quality for selecting the pose candidates.
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 • 27 Dec 2021 • Yabo Xiao, Xiaojuan Wang, Dongdong Yu, Guoli Wang, Qian Zhang, Mingshu He
Multi-person pose estimation methods generally follow top-down and bottom-up paradigms, both of which can be considered as two-stage approaches thus leading to the high computation cost and low efficiency.
no code implementations • 13 Apr 2020 • Yabo Xiao, Dongdong Yu, Xiaojuan Wang, Tianqi Lv, Yiqi Fan, Lingrui Wu
To alleviate these issues, we propose a novel Spatial Preserve and Content-aware Network(SPCNet), which includes two effective modules: Dilated Hourglass Module(DHM) and Selective Information Module(SIM).