Human Motion Prediction, Reconstruction, and Generation

TL;DR

This paper reviews recent advances in human motion prediction, reconstruction, and generation, emphasizing new models, challenges, and applications in robotics, gaming, and virtual reality.

Contribution

It provides a comprehensive overview of recent methodologies, datasets, and future directions in human motion modeling, highlighting innovations like transformer-based and diffusion models.

Findings

Transformer-based architectures improve reconstruction accuracy.

Diffusion models enhance motion generation diversity.

Text-to-motion methods enable fine-grained synthesis.

Abstract

This report reviews recent advancements in human motion prediction, reconstruction, and generation. Human motion prediction focuses on forecasting future poses and movements from historical data, addressing challenges like nonlinear dynamics, occlusions, and motion style variations. Reconstruction aims to recover accurate 3D human body movements from visual inputs, often leveraging transformer-based architectures, diffusion models, and physical consistency losses to handle noise and complex poses. Motion generation synthesizes realistic and diverse motions from action labels, textual descriptions, or environmental constraints, with applications in robotics, gaming, and virtual avatars. Additionally, text-to-motion generation and human-object interaction modeling have gained attention, enabling fine-grained and context-aware motion synthesis for augmented reality and robotics. This review highlights key methodologies, datasets, challenges, and future research directions driving progress in these fields.