# MoME: Estimating Psychological Traits from Gait with Multi-Stage Mixture of Movement Experts

arXiv: https://arxiv.org/abs/2510.04654 · HTML: https://mlsanity.com/paper/2510.04654/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 70 references that are in the map.
- First posted on arXiv 6 Oct 2025.

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

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://mlsanity.com/paper/2107.06278/) (2021)
- [ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation](https://mlsanity.com/paper/2204.12484/) (2022)
- [Learning Factored Representations in a Deep Mixture of Experts](https://mlsanity.com/paper/1312.4314/) (2013)
- [GaitSet: Regarding Gait as a Set for Cross-View Gait Recognition](https://mlsanity.com/paper/1811.06186/) (2018)
- [GaitGraph: Graph Convolutional Network for Skeleton-Based Gait Recognition](https://mlsanity.com/paper/2101.11228/) (2021)
- [Gait Recognition in the Wild with Dense 3D Representations and A Benchmark](https://mlsanity.com/paper/2204.02569/) (2022)
- [Stronger, Faster and More Explainable: A Graph Convolutional Baseline for Skeleton-based Action Recognition](https://mlsanity.com/paper/2010.09978/) (2020)
- [ViTPose++: Vision Transformer for Generic Body Pose Estimation](https://mlsanity.com/paper/2212.04246/) (2022)
- [GaitSet: Cross-view Gait Recognition through Utilizing Gait as a Deep Set](https://mlsanity.com/paper/2102.03247/) (2021)
- [Towards a Deeper Understanding of Skeleton-based Gait Recognition](https://mlsanity.com/paper/2204.07855/) (2022)
- [A Modulation Module for Multi-task Learning with Applications in Image Retrieval](https://mlsanity.com/paper/1807.06708/) (2018)
- [Exploring Deep Models for Practical Gait Recognition](https://mlsanity.com/paper/2303.03301/) (2023)
- [SkeletonGait: Gait Recognition Using Skeleton Maps](https://mlsanity.com/paper/2311.13444/) (2023)
- [DyGait: Exploiting Dynamic Representations for High-performance Gait Recognition](https://mlsanity.com/paper/2303.14953/) (2023)
- [GaitGL: Learning Discriminative Global-Local Feature Representations for Gait Recognition](https://mlsanity.com/paper/2208.01380/) (2022)
- [JointsGait:A model-based Gait Recognition Method based on Gait Graph Convolutional Networks and Joints Relationship Pyramid Mapping](https://mlsanity.com/paper/2005.08625/) (2020)
- [It's Just a Matter of Time: Detecting Depression with Time-Enriched Multimodal Transformers](https://mlsanity.com/paper/2301.05453/) (2023)
- [OpenGait: A Comprehensive Benchmark Study for Gait Recognition towards Better Practicality](https://mlsanity.com/paper/2405.09138/) (2024)
- [GaitPT: Skeletons Are All You Need For Gait Recognition](https://mlsanity.com/paper/2308.10623/) (2023)
- [Reading Between the Frames: Multi-Modal Depression Detection in Videos from Non-Verbal Cues](https://mlsanity.com/paper/2401.02746/) (2024)
- [PsyMo: A Dataset for Estimating Self-Reported Psychological Traits from Gait](https://mlsanity.com/paper/2308.10631/) (2023)
- [WildGait: Learning Gait Representations from Raw Surveillance Streams](https://mlsanity.com/paper/2105.05528/) (2021)
- [GaitFormer: Learning Gait Representations with Noisy Multi-Task Learning](https://mlsanity.com/paper/2310.19418/) (2023)
- [On Model and Data Scaling for Skeleton-based Self-Supervised Gait Recognition](https://mlsanity.com/paper/2504.07598/) (2025)

## Abstract (arXiv)

Gait encodes rich biometric and behavioural information, yet leveraging the manner of walking to infer psychological traits remains a challenging and underexplored problem. We introduce a hierarchical Multi-Stage Mixture of Movement Experts (MoME) architecture for multi-task prediction of psychological attributes from gait sequences represented as 2D poses. MoME processes the walking cycle in four stages of movement complexity, employing lightweight expert models to extract spatio-temporal features and task-specific gating modules to adaptively weight experts across traits and stages. Evaluated on the PsyMo benchmark covering 17 psychological traits, our method outperforms state-of-the-art gait analysis models, achieving a 37.47% weighted F1 score at the run level and 44.6% at the subject level. Our experiments show that integrating auxiliary tasks such as identity recognition, gender prediction, and BMI estimation further improves psychological trait estimation. Our findings demonstrate the viability of multi-task gait-based learning for psychological trait estimation and provide a foundation for future research on movement-informed psychological inference.