Motion-Lie Transformer: Geometric Attention For 3D Human Pose Motion Prediction

Abstract

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Skeletal motion prediction aims to forecast future movement based on 3D skeleton sequences, crucial for applications such as autonomous driving and virtual reality. However, anticipating the motion of 3D articulated objects is challenging due to their inherent non linearity and stochastic nature. Existing approaches often represent the skeleton as a set of 3D joints, which unfortunately ignores joint relationships and anatomical constraints. Moreover, conventional recurrent neural networks struggle with capturing long-term dependencies in motion contexts. To address these limitations, we propose encoding anatomical constraints through Lie algebra representation, integrating self-attention in transformer networks. Our Motion-Lie Transformer architecture, leveraging Transformers with self-attention, preserves human motion kinematics. Empirical evaluations on datasets like Human3.6M, GTA-IM, and PROX promise competitive performance and accurate 3D human pose estimation.