Pose2Mesh: Graph Convolutional Network for 3D Human Pose and Mesh Recovery from a 2D Human Pose
Autor: | Gyeongsik Moon, Kyoung Mu Lee, Hongsuk Choi |
---|---|
Rok vydání: | 2020 |
Předmět: |
Training set
business.industry Computer science Deep learning Mesh networking Inference 020206 networking & telecommunications 02 engineering and technology Overfitting Convolutional neural network 0202 electrical engineering electronic engineering information engineering Graph (abstract data type) 020201 artificial intelligence & image processing Computer vision Artificial intelligence business Estimation methods |
Zdroj: | Computer Vision – ECCV 2020 ISBN: 9783030585709 ECCV (7) |
Popis: | Most of the recent deep learning-based 3D human pose and mesh estimation methods regress the pose and shape parameters of human mesh models, such as SMPL and MANO, from an input image. The first weakness of these methods is the overfitting to image appearance, due to the domain gap between the training data captured from controlled settings such as a lab, and in-the-wild data in inference time. The second weakness is that the estimation of the pose parameters is quite challenging due to the representation issues of 3D rotations. To overcome the above weaknesses, we propose Pose2Mesh, a novel graph convolutional neural network (GraphCNN)-based system that estimates the 3D coordinates of human mesh vertices directly from the 2D human pose. The 2D human pose as input provides essential human body articulation information without image appearance. Also, the proposed system avoids the representation issues, while fully exploiting the mesh topology using GraphCNN in a coarse-to-fine manner. We show that our Pose2Mesh significantly outperforms the previous 3D human pose and mesh estimation methods on various benchmark datasets. The codes are publicly available(https://github.com/hongsukchoi/Pose2Mesh_RELEASE). |
Databáze: | OpenAIRE |
Externí odkaz: |