3D real-time human reconstruction with a single RGBD camera.
- Publisher:
- SPRINGER
- Publication Type:
- Journal Article
- Citation:
- Appl Intell (Dordr), 2023, 53, (8), pp. 8735-8745
- Issue Date:
- 2023
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| 3D real-time human reconstruction with a single RGBD camera.pdf | 1.86 MB |
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3D human reconstruction is an important technology connecting the real world and the virtual world, but most of previous work needs expensive computing resources, making it difficult in real-time scenarios. We propose a lightweight human body reconstruction system based on parametric model, which employs only one RGBD camera as input. To generate a human model end to end, we build a fast and lightweight deep-learning network named Fast Body Net (FBN). The network pays more attention on the face and hands to enrich the local details. Additionally, we train a denoising auto-encoder to reduce unreasonable states of human model. Due to the lack of human dataset based on RGBD images, we propose an Indoor-Human dataset to train the network, which contains a total of 2500 frames of action data of five actors collected by Azure Kinect camera. Depth images avoid using RGB to extract depth features, which makes FBN lightweight and high-speed in reconstructing parametric human model. Qualitative and quantitative analysis on experimental results show that our method can improve at least 57% in efficiency with similar accuracy, as compared to state-of-the-art methods. Through our study, it is also demonstrated that consumer-grade RGBD cameras can provide great applications in real-time display and interaction for virtual reality.
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