Recovering Accurate 3D Human Pose in the Wild Using IMUs and a Moving Camera

By Timo von Marcard, Roberto Henschel, Michael J. Black, Bodo Rosenhahn, Gerard Pons-Moll. European Conference on Computer Vision, 2018

550 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Endocrinology, Diabetes and Metabolism

View Paper: https://www.semanticscholar.org/paper/8cb53977a8f536376a50ecf3df503aa5aa07feb9

Citations in the corpus

• Deep 3D human pose estimation: A review (2021, unknown): “Recently, some methods (Trumble et al., 2017; von Marcard et al., 2018) have been proposed to solve this problem, and to a certain extent, these methods have made some progress.”
• CLIFF: Carrying Location Information in Full Frames into Human Pose and Shape Estimation (2022, unknown): “However, these annotations are scarce for inthe-wild datasets, since they require specialized devices and cost a lot of time and labor [14,32].”
• D3D-HOI: Dynamic 3D Human-Object Interactions from Videos (2021, unknown): “On the other hand, regression-based methods [11, 25, 26] rely on deep neural networks and large amounts of training data [8, 23] to directly predict the 3D pose of the human.”
• Accurate 3D Human Pose in The Wild Using IMUs and a Moving Camera (2018, unknown): “This document contains additional experiments to the paper ”Recovering Accurate 3D Human Pose in The Wild Using IMUs and a Moving Camera”[1].”
• TAP-Vid: A Benchmark for Tracking Any Point in a Video (2022, unknown): “Semantic Keypoint Tracking typically chooses a (very small) set of object categories and hand-defines a small number of keypoints to track [29, 50, 60, 64, 70, 80], though some variants track densely for humans [19, 44].”