BXB Yu, Z Zhang, Y Liu, S Zhong… - Proceedings of the …, 2023 - openaccess.thecvf.com
Abstract 3D human pose estimation has been researched for decades with promising fruits. 3D human pose lifting is one of the promising research directions toward the task where …
DH Hwang, S Kim, N Monet… - Proceedings of the …, 2020 - openaccess.thecvf.com
We present MoVNect, a lightweight deep neural network to capture 3D human pose using a single RGB camera. To improve the overall performance of the model, we apply the teacher …
Pose estimation plays a critical role in human-centered vision applications. However, it is difficult to deploy state-of-the-art HRNet-based pose estimation models on resource …
Estimating 3D humans from images often produces implausible bodies that lean, float, or penetrate the floor. Such methods ignore the fact that bodies are typically supported by the …
Y Xue, J Chen, X Gu, H Ma, H Ma - IEEE Transactions on Image …, 2022 - ieeexplore.ieee.org
Monocular 3D human pose estimation is challenging due to depth ambiguity. Convolution- based and Graph-Convolution-based methods have been developed to extract 3D …
Even though most existing monocular 3D pose estimation approaches achieve very competitive results, they ignore the heterogeneity among human body parts by estimating …
Regression-based methods for 3D human pose estimation directly predict the 3D pose parameters from a 2D image using deep networks. While achieving state-of-the-art …
To improve the generalization of 3D human pose estimators, many existing deep learning based models focus on adding different augmentations to training poses. However, data …
C Luo, X Chu, A Yuille - arXiv preprint arXiv:1811.04989, 2018 - arxiv.org
In this paper, we propose a fully convolutional network for 3D human pose estimation from monocular images. We use limb orientations as a new way to represent 3D poses and bind …