Our method works like the following: First, to model the multi-human environment, it processes multi-human 2D poses and builds a novel heterogeneous graph, where nodes from different people and within one person are connected to capture inter-human interactions and draw the body geometry (i. e., skeleton and mesh structure).
Ranked #3 on 3D Multi-Person Pose Estimation on MuPoTS-3D
Visual representations underlie object recognition tasks, but they often contain both robust and non-robust features.
Generalization of deep neural networks remains one of the main open problems in machine learning.
We present COCO-MEBOW (Monocular Estimation of Body Orientation in the Wild), a new large-scale dataset for orientation estimation from a single in-the-wild image.
no code implementations • 26 Feb 2020 • Xiaoyu Sun, Nathaniel J. Krakauer, Alexander Politowicz, Wei-Ting Chen, Qiying Li, Zuoyi Li, Xianjia Shao, Alfred Sunaryo, Mingren Shen, James Wang, Dane Morgan
To further explore GBDL models, we collected the largest flash point dataset to date, which contains 10575 unique molecules.