We present imGHUM, the first holistic generative model of 3D human shape and articulated pose, represented as a signed distance function.
Inspired by this, we propose a novel semi-supervised framework based on pseudo-labeling for outdoor 3D object detection tasks.
After HOpt, the training cost of ANN and RFR is increased more than that of the GPR and SVM.
We present a statistical, articulated 3D human shape modeling pipeline, within a fully trainable, modular, deep learning framework.
Monocular 3D human pose and shape estimation is challenging due to the many degrees of freedom of the human body and thedifficulty to acquire training data for large-scale supervised learning in complex visual scenes.
A DigitalMicrograph script InsteaDMatic has been developed to facilitate rapid automated continuous rotation electron diffraction (cRED) data acquisition.