# A Survey on Deep Learning for Skeleton-Based Human Animation

arXiv: https://arxiv.org/abs/2110.06901 · HTML: https://mlsanity.com/paper/2110.06901/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2021.

## Built on by (most cited first, 27 of 27)

- [Human Motion Generation: A Survey](https://mlsanity.com/paper/2307.10894/) (2023)
- [PhysHOI: Physics-Based Imitation of Dynamic Human-Object Interaction](https://mlsanity.com/paper/2312.04393/) (2023)
- [Human-Art: A Versatile Human-Centric Dataset Bridging Natural and Artificial Scenes](https://mlsanity.com/paper/2303.02760/) (2023)
- [MoConVQ: Unified Physics-Based Motion Control via Scalable Discrete Representations](https://mlsanity.com/paper/2310.10198/) (2023)
- [MoCapAct: A Multi-Task Dataset for Simulated Humanoid Control](https://mlsanity.com/paper/2208.07363/) (2022)
- [F-HOI: Toward Fine-grained Semantic-Aligned 3D Human-Object Interactions](https://mlsanity.com/paper/2407.12435/) (2024)
- [GANimator: Neural Motion Synthesis from a Single Sequence](https://mlsanity.com/paper/2205.02625/) (2022)
- [Precise Action-to-Video Generation Through Visual Action Prompts](https://mlsanity.com/paper/2508.13104/) (2025)
- [Survey on Modeling of Human-made Articulated Objects](https://mlsanity.com/paper/2403.14937/) (2024)
- [Skinned Motion Retargeting with Dense Geometric Interaction Perception](https://mlsanity.com/paper/2410.20986/) (2024)
- [Didn't see that coming: a survey on non-verbal social human behavior forecasting](https://mlsanity.com/paper/2203.02480/) (2022)
- [A Survey on Reinforcement Learning Methods in Character Animation](https://mlsanity.com/paper/2203.04735/) (2022)
- [From Universal Humanoid Control to Automatic Physically Valid Character Creation](https://mlsanity.com/paper/2206.09286/) (2022)
- [Learning to Get Up](https://mlsanity.com/paper/2205.00307/) (2022)
- [LEAD: Latent Realignment for Human Motion Diffusion](https://mlsanity.com/paper/2410.14508/) (2024)
- [Text-driven Motion Generation: Overview, Challenges and Directions](https://mlsanity.com/paper/2505.09379/) (2025)
- [Taming Diffusion Models for Music-driven Conducting Motion Generation](https://mlsanity.com/paper/2306.10065/) (2023)
- [SMPL-IK: Learned Morphology-Aware Inverse Kinematics for AI Driven Artistic Workflows](https://mlsanity.com/paper/2208.08274/) (2022)
- [Two-Person Interaction Augmentation with Skeleton Priors](https://mlsanity.com/paper/2404.05490/) (2024)
- [Pose Representations for Deep Skeletal Animation](https://mlsanity.com/paper/2111.13907/) (2021)
- [AStF: Motion Style Transfer via Adaptive Statistics Fusor](https://mlsanity.com/paper/2511.04192/) (2025)
- [Adversarial Attention for Human Motion Synthesis](https://mlsanity.com/paper/2204.11751/) (2022)
- [Paved2Paradise: Cost-Effective and Scalable LiDAR Simulation by Factoring the Real World](https://mlsanity.com/paper/2312.01117/) (2023)
- [Causal Graph Neural Networks for Healthcare](https://mlsanity.com/paper/2511.02531/) (2025)
- [Video Understanding by Design: How Datasets Shape Video Models](https://mlsanity.com/paper/2509.09151/) (2025)
- [TeMuDance: Contrastive Alignment-Based Textual Control for Music-Driven Dance Generation](https://mlsanity.com/paper/2604.17005/) (2026)
- [Parasitic Co-Denoising: Unlocking 3D Human Motion Generation in a Frozen Video Diffusion Model](https://mlsanity.com/paper/2610.03047/) (2026)

## Abstract (arXiv)

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.