3D Human Motion Prediction

3D human motion prediction aims to forecast future human poses from observed motion sequences, a crucial task for applications like human-robot interaction and autonomous driving. Current research emphasizes improving prediction accuracy and realism, particularly addressing the inherent uncertainty and multi-modality of human movement, using advanced architectures such as transformers, diffusion models, and graph convolutional networks. These models are being refined to handle long-term predictions, incorporate uncertainty quantification, and achieve real-time performance, significantly impacting fields requiring accurate and efficient human behavior understanding.