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

翻译：本文聚焦于可变形三维物体（如人体运动）点云序列的运动预测这一具有挑战性的问题。首先，我们研究了此类表示中由可变形形状和复杂运动引发的技术难点，旨在深入理解当前最先进模型的技术局限性。基于此认知，我们提出了一种改进型架构用于可变形三维物体的点云预测。具体而言，为处理可变形形状，我们提出基于图的方法，通过学习并挖掘点云的空间结构以提取更具代表性的特征。随后，我们设计了一个模块，能够根据点云运动以自适应方式融合所学习的特征。该自适应模块可控制每个点的局部与全局运动组合，使网络更有效地建模可变形三维物体的复杂运动。我们在以下数据集上测试了所提方法：MNIST移动数字、Mixamo人体运动、JPEG及CWIPC-SXR真实动态体。仿真结果表明，该方法在建模复杂运动与保持点云形状方面均优于现有基线方法。此外，通过在MSRAction3D数据集上进行动作识别测试，我们验证了所提框架在动态特征学习中的泛化能力，并取得了与最先进方法相当的性能。