Toward Marker-free 3D Pose Estimation in Lifting: A Deep Multi-view Solution
arXiv:1802.01741
Abstract
Lifting is a common manual material handling task performed in the workplaces. It is considered as one of the main risk factors for Work-related Musculoskeletal Disorders. To improve work place safety, it is necessary to assess musculoskeletal and biomechanical risk exposures associated with these tasks, which requires very accurate 3D pose. Existing approaches mainly utilize marker-based sensors to collect 3D information. However, these methods are usually expensive to setup, time-consuming in process, and sensitive to the surrounding environment. In this study, we propose a multi-view based deep perceptron approach to address aforementioned limitations. Our approach consists of two modules: a "view-specific perceptron" network extracts rich information independently from the image of view, which includes both 2D shape and hierarchical texture information; while a "multi-view integration" network synthesizes information from all available views to predict accurate 3D pose. To fully evaluate our approach, we carried out comprehensive experiments to compare different variants of our design. The results prove that our approach achieves comparable performance with former marker-based methods, i.e. an average error of mm on the lifting dataset. The results are also compared with state-of-the-art methods on HumanEva-I dataset, which demonstrates the superior performance of our approach.
arXiv commentFG2018, accepted as a long paper
References in corpus (8)
- Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image
- Stacked Hourglass Networks for Human Pose Estimation
- Image De-raining Using a Conditional Generative Adversarial Network
- MoCap-guided Data Augmentation for 3D Pose Estimation in the Wild
- Generative Adversarial Network-based Synthesis of Visible Faces from Polarimetric Thermal Faces
- A Recurrent Encoder-Decoder Network for Sequential Face Alignment
- Reconstruction-Based Disentanglement for Pose-invariant Face Recognition
- 3D Human Pose Estimation = 2D Pose Estimation + Matching