# Generative Adversarial Graph Convolutional Networks for Human Action Synthesis

arXiv: https://arxiv.org/abs/2110.11191 · HTML: https://mlsanity.com/paper/2110.11191/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 50 references that are in the map.
- First posted on arXiv 21 Oct 2021.

## Builds on (most cited first, 30 of 33)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Glow: Generative Flow with Invertible 1x1 Convolutions](https://mlsanity.com/paper/1807.03039/) (2018)
- [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks](https://mlsanity.com/paper/1809.00219/) (2018)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [Interpreting the Latent Space of GANs for Semantic Face Editing](https://mlsanity.com/paper/1907.10786/) (2019)
- [NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis](https://mlsanity.com/paper/1604.02808/) (2016)
- [NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding](https://mlsanity.com/paper/1905.04757/) (2019)
- [Unsupervised Discovery of Interpretable Directions in the GAN Latent Space](https://mlsanity.com/paper/2002.03754/) (2020)
- [HP-GAN: Probabilistic 3D human motion prediction via GAN](https://mlsanity.com/paper/1711.09561/) (2017)
- [Isolating Sources of Disentanglement in Variational Autoencoders](https://mlsanity.com/paper/1802.04942/) (2018)
- [Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition](https://mlsanity.com/paper/1607.07043/) (2016)
- [Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition](https://mlsanity.com/paper/1904.01189/) (2019)
- [An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data](https://mlsanity.com/paper/1611.06067/) (2016)
- [A New Representation of Skeleton Sequences for 3D Action Recognition](https://mlsanity.com/paper/1703.03492/) (2017)
- [Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation](https://mlsanity.com/paper/1804.06055/) (2018)
- [View Adaptive Recurrent Neural Networks for High Performance Human Action Recognition from Skeleton Data](https://mlsanity.com/paper/1703.08274/) (2017)
- [Interpretable 3D Human Action Analysis with Temporal Convolutional Networks](https://mlsanity.com/paper/1704.04516/) (2017)
- [Deep Video Generation, Prediction and Completion of Human Action Sequences](https://mlsanity.com/paper/1711.08682/) (2017)
- [Hierarchical Long-term Video Prediction without Supervision](https://mlsanity.com/paper/1806.04768/) (2018)
- [Learning Diverse Stochastic Human-Action Generators by Learning Smooth Latent Transitions](https://mlsanity.com/paper/1912.10150/) (2019)
- [Deep Generative Adversarial Compression Artifact Removal](https://mlsanity.com/paper/1704.02518/) (2017)

## Built on by (most cited first, 23 of 23)

- [Human Motion Generation: A Survey](https://mlsanity.com/paper/2307.10894/) (2023)
- [Single Motion Diffusion](https://mlsanity.com/paper/2302.05905/) (2023)
- [MoDi: Unconditional Motion Synthesis from Diverse Data](https://mlsanity.com/paper/2206.08010/) (2022)
- [Modiff: Action-Conditioned 3D Motion Generation with Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2301.03949/) (2023)
- [Continuous Intermediate Token Learning with Implicit Motion Manifold for Keyframe Based Motion Interpolation](https://mlsanity.com/paper/2303.14926/) (2023)
- [StickMotion: Generating 3D Human Motions by Drawing a Stickman](https://mlsanity.com/paper/2503.04829/) (2025)
- [You Think, You ACT: The New Task of Arbitrary Text to Motion Generation](https://mlsanity.com/paper/2404.14745/) (2024)
- [Motion Generation: A Survey of Generative Approaches and Benchmarks](https://mlsanity.com/paper/2507.05419/) (2025)
- [Token Boosting for Robust Self-Supervised Visual Transformer Pre-training](https://mlsanity.com/paper/2304.04175/) (2023)
- [SpeechAct: Towards Generating Whole-body Motion from Speech](https://mlsanity.com/paper/2311.17425/) (2023)
- [FlowMotion: Target-Predictive Conditional Flow Matching for Jitter-Reduced Text-Driven Human Motion Generation](https://mlsanity.com/paper/2504.01338/) (2025)
- [ViMo: Generating Motions from Casual Videos](https://mlsanity.com/paper/2408.06614/) (2024)
- [Language-free Compositional Action Generation via Decoupling Refinement](https://mlsanity.com/paper/2307.03538/) (2023)
- [PlanMoGPT: Flow-Enhanced Progressive Planning for Text to Motion Synthesis](https://mlsanity.com/paper/2506.17912/) (2025)
- [Multi-Modal Motion Retrieval by Learning a Fine-Grained Joint Embedding Space](https://mlsanity.com/paper/2507.23188/) (2025)
- [Riemannian Motion Generation: A Unified Framework for Human Motion Representation and Generation via Riemannian Flow Matching](https://mlsanity.com/paper/2603.15016/) (2026)
- [Motion Generation Review: Exploring Deep Learning for Lifelike Animation with Manifold](https://mlsanity.com/paper/2412.10458/) (2024)
- [TextIM: Part-aware Interactive Motion Synthesis from Text](https://mlsanity.com/paper/2408.03302/) (2024)
- [Generative AI-Driven High-Fidelity Human Motion Simulation](https://mlsanity.com/paper/2507.14097/) (2025)
- [Kinetic Mining in Context: Few-Shot Action Synthesis via Text-to-Motion Distillation](https://mlsanity.com/paper/2512.11654/) (2025)
- [PMG: Parameterized Motion Generator for Human-like Locomotion Control](https://mlsanity.com/paper/2602.12656/) (2026)
- [DrawMotion: Generating 3D Human Motions by Freehand Drawing](https://mlsanity.com/paper/2605.20955/) (2026)
- [Training-free Controllable Human Motion Generation under Heterogeneous Constraints](https://mlsanity.com/paper/2607.01990/) (2026)

## Abstract (arXiv)

Synthesising the spatial and temporal dynamics of the human body skeleton remains a challenging task, not only in terms of the quality of the generated shapes, but also of their diversity, particularly to synthesise realistic body movements of a specific action (action conditioning). In this paper, we propose Kinetic-GAN, a novel architecture that leverages the benefits of Generative Adversarial Networks and Graph Convolutional Networks to synthesise the kinetics of the human body. The proposed adversarial architecture can condition up to 120 different actions over local and global body movements while improving sample quality and diversity through latent space disentanglement and stochastic variations. Our experiments were carried out in three well-known datasets, where Kinetic-GAN notably surpasses the state-of-the-art methods in terms of distribution quality metrics while having the ability to synthesise more than one order of magnitude regarding the number of different actions. Our code and models are publicly available at https://github.com/DegardinBruno/Kinetic-GAN.