# Adversarial Attention for Human Motion Synthesis

arXiv: https://arxiv.org/abs/2204.11751 · HTML: https://mlsanity.com/paper/2204.11751/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 57 references that are in the map.
- First posted on arXiv 25 Apr 2022.

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

- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Self-Attention Generative Adversarial Networks](https://mlsanity.com/paper/1805.08318/) (2018)
- [Wasserstein GAN](https://mlsanity.com/paper/1701.07875/) (2017)
- [Adversarial Autoencoders](https://mlsanity.com/paper/1511.05644/) (2015)
- [On human motion prediction using recurrent neural networks](https://mlsanity.com/paper/1705.02445/) (2017)
- [Structural-RNN: Deep Learning on Spatio-Temporal Graphs](https://mlsanity.com/paper/1511.05298/) (2015)
- [Robust Motion In-betweening](https://mlsanity.com/paper/2102.04942/) (2021)
- [DLow: Diversifying Latent Flows for Diverse Human Motion Prediction](https://mlsanity.com/paper/2003.08386/) (2020)
- [Character Controllers Using Motion VAEs](https://mlsanity.com/paper/2103.14274/) (2021)
- [HP-GAN: Probabilistic 3D human motion prediction via GAN](https://mlsanity.com/paper/1711.09561/) (2017)
- [Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification](https://mlsanity.com/paper/1801.02385/) (2018)
- [A Spatio-temporal Transformer for 3D Human Motion Prediction](https://mlsanity.com/paper/2004.08692/) (2020)
- [Dynamic Multiscale Graph Neural Networks for 3D Skeleton-Based Human Motion Prediction](https://mlsanity.com/paper/2003.08802/) (2020)
- [Context-aware Human Motion Prediction](https://mlsanity.com/paper/1904.03419/) (2019)
- [Learning Human Motion Models for Long-term Predictions](https://mlsanity.com/paper/1704.02827/) (2017)
- [Action-Agnostic Human Pose Forecasting](https://mlsanity.com/paper/1810.09676/) (2018)
- [Socially and Contextually Aware Human Motion and Pose Forecasting](https://mlsanity.com/paper/2007.06843/) (2020)
- [Symbiotic Graph Neural Networks for 3D Skeleton-based Human Action Recognition and Motion Prediction](https://mlsanity.com/paper/1910.02212/) (2019)
- [Spatiotemporal Co-attention Recurrent Neural Networks for Human-Skeleton Motion Prediction](https://mlsanity.com/paper/1909.13245/) (2019)
- [Combining Recurrent Neural Networks and Adversarial Training for Human Motion Synthesis and Control](https://mlsanity.com/paper/1806.08666/) (2018)
- [Human Motion Prediction via Learning Local Structure Representations and Temporal Dependencies](https://mlsanity.com/paper/1902.07367/) (2019)
- [Plug-and-Play CNN for Crowd Motion Analysis: An Application in Abnormal Event Detection](https://mlsanity.com/paper/1610.00307/) (2016)
- [Disentangling Human Dynamics for Pedestrian Locomotion Forecasting with Noisy Supervision](https://mlsanity.com/paper/1911.01138/) (2019)
- [A Survey on Deep Learning for Skeleton-Based Human Animation](https://mlsanity.com/paper/2110.06901/) (2021)
- [GAN-based Reactive Motion Synthesis with Class-aware Discriminators for Human-human Interaction](https://mlsanity.com/paper/2110.00380/) (2021)
- [Generative Choreography using Deep Learning](https://mlsanity.com/paper/1605.06921/) (2016)
- [Controllable Generative Adversarial Network](https://mlsanity.com/paper/1708.00598/) (2017)
- [Towards Explainable, Privacy-Preserved Human-Motion Affect Recognition](https://mlsanity.com/paper/2105.03958/) (2021)

## Built on by (most cited first, 1 of 1)

- [Towards Motion Turing Test: Evaluating Human-Likeness in Humanoid Robots](https://mlsanity.com/paper/2603.06181/) (2026)

## Abstract (arXiv)

Analysing human motions is a core topic of interest for many disciplines, from Human-Computer Interaction, to entertainment, Virtual Reality and healthcare. Deep learning has achieved impressive results in capturing human pose in real-time. On the other hand, due to high inter-subject variability, human motion analysis models often suffer from not being able to generalise to data from unseen subjects due to very limited specialised datasets available in fields such as healthcare. However, acquiring human motion datasets is highly time-consuming, challenging, and expensive. Hence, human motion synthesis is a crucial research problem within deep learning and computer vision. We present a novel method for controllable human motion synthesis by applying attention-based probabilistic deep adversarial models with end-to-end training. We show that we can generate synthetic human motion over both short- and long-time horizons through the use of adversarial attention. Furthermore, we show that we can improve the classification performance of deep learning models in cases where there is inadequate real data, by supplementing existing datasets with synthetic motions.