From an image of a person in action, we can easily guess the 3D motion of the person in the
immediate past and future. This is because we have a mental model of 3D human dynamics …

The premise of training an accurate 3D human pose estimation network is the possession of
huge amount of richly annotated training data. Nonetheless, manually obtaining rich and …

Estimating 3D poses from a monocular video is still a challenging task, despite the
significant progress that has been made in the recent years. Generally, the performance of …

In this work, we demonstrate that 3D poses in video can be effectively estimated with a fully
convolutional model based on dilated temporal convolutions over 2D keypoints. We also …

We present deep neural network methodology to reconstruct the 3d pose and shape of
people, including hand gestures and facial expression, given an input RGB image. We rely …

Event camera is an emerging imaging sensor for capturing dynamics of moving objects as
events, which motivates our work in estimating 3D human pose and shape from the event …

Learning to capture human motion is essential to 3D human pose and shape estimation
from monocular video. However, the existing methods mainly rely on recurrent or …

In this paper, we study the task of 3D human pose estimation in the wild. This task is
challenging due to lack of training data, as existing datasets are either in the wild images …

This paper addresses the problem of cross-dataset generalization of 3D human pose
estimation models. Testing a pre-trained 3D pose estimator on a new dataset results in a …

We propose a new loss function, called motion loss, for supervising models for monocular
3D Human pose estimation from videos. It works by comparing the motion pattern of the …