| Paper ID | IVMSP-24.1 |
| Paper Title |
ADAPTIVE MULTI-DOMAIN LEARNING FOR OUTDOOR 3D HUMAN POSE AND SHAPE ESTIMATION |
| Authors |
Zhaoyang Gui, Shanshan Zhang, Kangkan Wang, Jian Yang, Nanjing University of Science and Technology, China; Pong Chi Yuen, Hong Kong Baptist University, China |
| Session | IVMSP-24: Applications 2 |
| Location | Gather.Town |
| Session Time: | Thursday, 10 June, 15:30 - 16:15 |
| Presentation Time: | Thursday, 10 June, 15:30 - 16:15 |
| Presentation |
Poster
|
| Topic |
Image, Video, and Multidimensional Signal Processing: [IVTEC] Image & Video Processing Techniques |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
It is an extremely challenging task to estimate 3D human pose and shape in outdoor scenes for which we can hardly obtain precise ground truth data for training. Previous methods usually use multiple datasets collected at different scenes to train their models, including those collected in laboratories with precise ground truth and those collected at outdoor scenes with estimated or even no ground truth. Since data from different scenes are included in training, it is necessary to handle the domain difference problem, which unfortunately has never been considered by previous works. In this paper, we first point out this problem and then address it via a novel cascade multi-domain learning module (CMDL), where multiple adapters are employed to extract more discriminative features for different domains. We show that our method with CMDL outperforms previous methods in outdoor scenes. In principle, the proposed CMDL module can be easily applied on top of any arbitrary 3D human pose and shape approach. |
