AGAR: Attention Graph-RNN for Adaptative Motion Prediction of Point
  Clouds of Deformable Objects
Pedro Gomes Silvia Rossi Laura Toni
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 an 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, JPEG
and CWIPC-SXR 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 and achieving results on-par with state-of-the-art methods