An Adaptive Viewpoint Transformation Network for 3D Human Pose Estimation

Abstract

Human pose estimation from a monocular image has attracted lots of interest due to its huge potential application in many areas. The performance of 2D human pose estimation has been improved a lot with the emergence of deep convolutional neural network. In contrast, the recovery of 3D human pose from an 2D pose is still a challenging problem. Currently, most of the methods try to learn a universal map, which can be applied for all human poses in any viewpoints. However, due to the large variety of human poses and camera viewpoints, it is very difficult to learn a such universal mapping from current datasets for 3D pose estimation. Instead of learning a universal map, we propose to learn an adaptive viewpoint transformation module, which transforms the 2D human pose to a more suitable viewpoint for recovering the 3D human pose. Specifically, our transformation module takes a 2D pose as input and predicts the transformation parameters. Rather than some hand-crafted criteria, this module is directly learned from the datasets and depends on the input 2D pose in testing phrase. Then the 3D pose is recovered from this transformed 2D pose. Since the difficulty of 3D pose recovery becomes smaller, we can obtain more accurate estimation results. Experiments on Human3.6M and MPII datasets show that the proposed adaptive viewpoint transformation can improve the performance of 3D human pose estimation. INDEX TERMS 3D human pose estimation, adaptive viewpoint transformation, deep convolutional neural network

Capturing Conversational Gestures for Embodied Conversational Agents Using an Optimized Kaneda–Lucas–Tomasi Tracker and Denavit–Hartenberg-Based Kinematic Model

“…Most conversational corpora consist of TV interviews and theatrical plays that have shown themselves to be an appropriate resource of spontaneous conversational expressions, and are significantly more suitable for research in wider ‘discourse concepts’ than any artificially recorded material [ 37 ]. Most methods related to 3D Pose and Shape Estimation from monocular sources refer to (Deep) Convolutional Neural Networks ((D) CNNs) and leverage 2D joint tracking and predict 3D joint poses in the form of stick figures [ 38 , 39 , 40 , 41 , 42 ]. The major challenge with deep learning and similar probabilistic approaches is that the tracking process involves predicting the most probable configuration of the artificial skeleton.…”

EvoPose2D: Pushing the Boundaries of 2D Human Pose Estimation Using Accelerated Neuroevolution With Weight Transfer

“…It is widely considered a fundamental problem in computer vision due to its many downstream applications, including action recognition [6]- [11] and human tracking [12]- [14]. In particular, it is a precursor to 3D human pose estimation [15]- [17], which serves as a potential alternative to invasive marker-based motion capture.…”