Gomes, Pedro de Medeiros;
Rossi, Silvia;
Toni, Laura;
(2024)
AGAR - Attention Graph-RNN for Adaptative Motion Prediction of Point Clouds of Deformable Objects.
ACM Transactions on Multimedia Computing, Communications, and Applications
10.1145/3662183.
(In press).
Preview |
Text
3662183.pdf - Accepted Version Download (3MB) | Preview |
Abstract
This paper focuses on motion prediction for point cloud sequences in the challenging case of deformable 3D objects, such as human body motion. First, we investigate the challenges caused by deformable shapes and complex motions present in this type of representation, with the ultimate goal of understanding the technical limitations of state-of-the-art models. From this understanding, we propose an improved architecture for point cloud prediction of deformable 3D objects. Specifically, to handle deformable shapes, we propose a graph-based approach that learns and exploits the spatial structure of point clouds to extract more representative features. Then, we propose a module able to combine the learned features in a adaptative manner according to the point cloud movements. The proposed adaptative module controls the composition of local and global motions for each point, enabling the network to model complex motions in deformable 3D objects more effectively. We tested the proposed method on the following datasets: MNIST moving digits, the Mixamo human bodies motions [15], JPEG [5] and CWIPC-SXR [32] real-world dynamic bodies. Simulation results demonstrate that our method outperforms the current baseline methods given its improved ability to model complex movements as well as preserve point cloud shape. Furthermore, we demonstrate the generalizability of the proposed framework for dynamic feature learning by testing the framework for action recognition on the MSRAction3D dataset [19] and achieving results on par with state-of-the-art methods.
| Type: | Article |
|---|---|
| Title: | AGAR - Attention Graph-RNN for Adaptative Motion Prediction of Point Clouds of Deformable Objects |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3662183 |
| Publisher version: | http://dx.doi.org/10.1145/3662183 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Point Cloud, Graph Neural Network, Motion Prediction, Explainability |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10191885 |
Archive Staff Only
 |
View Item |
