# Persistent-Transient Duality in Human Behavior Modeling

arXiv: https://arxiv.org/abs/2204.09875 · HTML: https://mlsanity.com/paper/2204.09875/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 4 references that are in the map.
- First posted on arXiv 21 Apr 2022.

## Builds on (most cited first, 2 of 2)

- [Structural-RNN: Deep Learning on Spatio-Temporal Graphs](https://mlsanity.com/paper/1511.05298/) (2015)
- [Context-aware Human Motion Prediction](https://mlsanity.com/paper/1904.03419/) (2019)

## Abstract (arXiv)

We propose to model the persistent-transient duality in human behavior using a parent-child multi-channel neural network, which features a parent persistent channel that manages the global dynamics and children transient channels that are initiated and terminated on-demand to handle detailed interactive actions. The short-lived transient sessions are managed by a proposed Transient Switch. The neural framework is trained to discover the structure of the duality automatically. Our model shows superior performances in human-object interaction motion prediction.