1vmp

2022

1. 

   Explicit Occlusion Reasoning for Multi-person 3D Human Pose Estimation

   Qihao Liu, Yi Zhang, Song Bai, and Alan Yuille

   In ECCV 2022

   Abs Qing Bib HTML

   Occlusion poses a great threat to monocular multi-person 3D human poseestimation due to large variability in terms of the shape, appearance, andposition of occluders. While existing methods try to handle occlusion with posepriors/constraints, data augmentation, or implicit reasoning, they still failto generalize to unseen poses or occlusion cases and may make large mistakeswhen multiple people are present. Inspired by the remarkable ability of humansto infer occluded joints from visible cues, we develop a method to explicitlymodel this process that significantly improves bottom-up multi-person humanpose estimation with or without occlusions. First, we split the task into twosubtasks: visible keypoints detection and occluded keypoints reasoning, andpropose a Deeply Supervised Encoder Distillation (DSED) network to solve thesecond one. To train our model, we propose a Skeleton-guided human ShapeFitting (SSF) approach to generate pseudo occlusion labels on the existingdatasets, enabling explicit occlusion reasoning. Experiments show thatexplicitly learning from occlusions improves human pose estimation. Inaddition, exploiting feature-level information of visible joints allows us toreason about occluded joints more accurately. Our method outperforms both thestate-of-the-art top-down and bottom-up methods on several benchmarks.

   估计被遮挡住的数据然后再进行association

   @inproceedings{Occulusion_reasoning,
     title = {Explicit Occlusion Reasoning for Multi-person 3D Human Pose Estimation},
     author = {Liu, Qihao and Zhang, Yi and Bai, Song and Yuille, Alan},
     year = {2022},
     tags = {human-pose-estimation, 1vmp},
     booktitle = {ECCV},
   }

   human-pose-estimation   1vmp  

2021

2020

2019

2018

2017