A Survey on Deep Learning for Skeleton-Based Human Animation

TL;DR

This survey reviews recent deep learning and reinforcement learning methods for skeleton-based human animation, covering data representations, applications, limitations, and future research directions.

Contribution

It provides a comprehensive overview of state-of-the-art deep learning approaches in human animation, categorizing applications and discussing current challenges and future prospects.

Findings

Deep learning models effectively learn spatial and temporal motion patterns.

Applications include motion synthesis, character control, and motion editing.

Current methods face limitations in realism and generalization.

Abstract

Human character animation is often critical in entertainment content production, including video games, virtual reality or fiction films. To this end, deep neural networks drive most recent advances through deep learning and deep reinforcement learning. In this article, we propose a comprehensive survey on the state-of-the-art approaches based on either deep learning or deep reinforcement learning in skeleton-based human character animation. First, we introduce motion data representations, most common human motion datasets and how basic deep models can be enhanced to foster learning of spatial and temporal patterns in motion data. Second, we cover state-of-the-art approaches divided into three large families of applications in human animation pipelines: motion synthesis, character control and motion editing. Finally, we discuss the limitations of the current state-of-the-art methods based on deep learning and/or deep reinforcement learning in skeletal human character animation and possible directions of future research to alleviate current limitations and meet animators' needs.