Humans are highly adaptable, swiftly switching between different modes to
progressively handle different tasks, situations and contexts. In Human-object
interaction (HOI) activities, these modes can be attributed to two mechanisms:
(1) the large-scale consistent plan for the whole activity and (2) the
small-scale children interactive actions that start and end along the timeline.
While neuroscience and cognitive science have confirmed this multi-mechanism
nature of human behavior, machine modeling approaches for human motion are
trailing behind. While attempted to use gradually morphing structures (e.g.,
graph attention networks) to model the dynamic HOI patterns, they miss the
expeditious and discrete mode-switching nature of the human motion. To bridge
that gap, this work proposes to model two concurrent mechanisms that jointly
control human motion: the Persistent process that runs continually on the
global scale, and the Transient sub-processes that operate intermittently on
the local context of the human while interacting with objects. These two
mechanisms form an interactive Persistent-Transient Duality that
synergistically governs the activity sequences. We model this conceptual
duality by a parent-child neural network of Persistent and Transient channels
with a dedicated neural module for dynamic mechanism switching. The framework
is trialed on HOI motion forecasting. On two rich datasets and a wide variety
of settings, the model consistently delivers superior performances, proving its
suitability for the challenge.

人类与物体的交互中，通过模型和神经网络控制人的动作机制的不断切换，以预测人的动作序列。

持久性与瞬时性：一种多机制建模人 - 物互动的方法

Persistent-Transient Duality: A Multi-mechanism Approach for Modeling  Human-Object Interaction

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.

我们提出使用父母 - 子女多通道神经网络来建模人类行为中的持久 - 瞬时二分性，该网络具有管理全局动态的父持久通道和用于处理详细交互操作的子瞬时通道。短暂的瞬时会话由 “瞬时转换器” 管理。该神经框架通过训练自动发现二元性质的结构，在人 - 物互动运动预测方面表现出优越性能。