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Found 5 papers, 2 papers with code
MUG: Multi-human Graph Network for 3D Mesh Reconstruction from 2D Pose
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
3D Multi-Person Human Pose Estimation
3D Multi-Person Pose Estimation
Causal Transportability for Visual Recognition
Visual representations underlie object recognition tasks, but they often contain both robust and non-robust features.
With Greater Distance Comes Worse Performance: On the Perspective of Layer Utilization and Model Generalization
Generalization of deep neural networks remains one of the main open problems in machine learning.
MEBOW: Monocular Estimation of Body Orientation In the Wild
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.
Assessing Graph-based Deep Learning Models for Predicting Flash Point
To further explore GBDL models, we collected the largest flash point dataset to date, which contains 10575 unique molecules.
