# Deep state-space modeling for explainable representation, analysis, and generation of professional human poses

arXiv: https://arxiv.org/abs/2304.14502 · HTML: https://mlsanity.com/paper/2304.14502/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 54 references that are in the map.
- First posted on arXiv 13 Apr 2023.

## Builds on (most cited first, 17 of 17)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)](https://mlsanity.com/paper/1711.11279/) (2017)
- [AMASS: Archive of Motion Capture as Surface Shapes](https://mlsanity.com/paper/1904.03278/) (2019)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [A Recurrent Latent Variable Model for Sequential Data](https://mlsanity.com/paper/1506.02216/) (2015)
- [Human Motion Trajectory Prediction: A Survey](https://mlsanity.com/paper/1905.06113/) (2019)
- [Sequential Neural Models with Stochastic Layers](https://mlsanity.com/paper/1605.07571/) (2016)
- [Generating Smooth Pose Sequences for Diverse Human Motion Prediction](https://mlsanity.com/paper/2108.08422/) (2021)
- [MoVi: A Large Multipurpose Motion and Video Dataset](https://mlsanity.com/paper/2003.01888/) (2020)
- [Spatiotemporal Co-attention Recurrent Neural Networks for Human-Skeleton Motion Prediction](https://mlsanity.com/paper/1909.13245/) (2019)
- [Multi-level Motion Attention for Human Motion Prediction](https://mlsanity.com/paper/2106.09300/) (2021)
- [DeepSSM: Deep State-Space Model for 3D Human Motion Prediction](https://mlsanity.com/paper/2005.12155/) (2020)
- [How Can I Swing Like Pro?: Golf Swing Analysis Tool for Self Training](https://mlsanity.com/paper/2105.10153/) (2021)
- [Motion Capture Benchmark of Real Industrial Tasks and Traditional Crafts for Human Movement Analysis](https://mlsanity.com/paper/2304.03771/) (2023)

## Abstract (arXiv)

The analysis of human movements has been extensively studied due to its wide variety of practical applications, such as human-robot interaction, human learning applications, or clinical diagnosis. Nevertheless, the state-of-the-art still faces scientific challenges when modeling human movements. To begin, new models must account for the stochasticity of human movement and the physical structure of the human body in order to accurately predict the evolution of full-body motion descriptors over time. Second, while utilizing deep learning algorithms, their explainability in terms of body posture predictions needs to be improved as they lack comprehensible representations of human movement. This paper addresses these challenges by introducing three novel methods for creating explainable representations of human movement. In this study, human body movement is formulated as a state-space model adhering to the structure of the Gesture Operational Model (GOM), whose parameters are estimated through the application of deep learning and statistical algorithms. The trained models are used for the full-body dexterity analysis of expert professionals, in which dynamic associations between body joints are identified, and for generating artificially professional movements.