Publikationsserver
Perception of Human Movement Based on Modular Movement Primitives
People can identify and understand human movement from very degraded visual information without effort. A few dots representing the position of the joints are enough to induce a vivid and stable percept of the underlying movement. Due to this ability, the realistic animation of 3D characters req...
Saved in:
| Main Author: | |
|---|---|
| Contributors: | |
| Format: | Doctoral Thesis |
| Language: | English |
| Published: |
Philipps-Universität Marburg
2021
|
| Subjects: | |
| Online Access: | PDF Full Text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | People can identify and understand human movement from very
degraded visual information without effort.
A few dots representing the position of the joints
are enough to induce a vivid and stable percept of the underlying movement.
Due to this ability, the realistic animation of 3D characters requires
great skill. Studying the constituents of movement that looks natural would not
only help these artists, but also bring better understanding of the
underlying information processing in the brain.
Analogous to the hurdles in animation, the efforts of roboticists reflect the
complexity of motion production: controlling the many degrees of freedom
of a body requires time-consuming computations.
Modularity is one strategy to address this problem:
Complex movement can be decomposed into simple primitives.
A few primitives can conversely be used to compose a
large number of movements.
Many types of movement primitives (MPs) have been proposed
on different levels of information processing hierarchy in
the brain.
MPs have mostly been proposed for movement production.
Yet, modularity based on primitives might similarly enable robust movement
perception.
For my thesis, I have conducted perceptual experiments based on
the assumption of a shared representation of perception and action
based on MPs.
The three different types of MPs I have investigated are
temporal MPs (TMP), dynamical MPs (DMP), and coupled
Gaussian process dynamical models (cGPDM).
The MP-models have been trained on natural movements
to generate new movements. I then perceptually validated these
artificial movements in different psychophysical experiments.
In all experiments I used a two-alternative forced choice paradigm,
in which human observers were presented a movement based on
motion-capturing data, and one generated by an MP-model.
They were then asked to chose the movement which they perceived
as more natural.
In the first experiment I investigated walking movements, and
found that, in line with previous results, faithful representation
of movement dynamics is more important than good reconstruction
of pose.
In the second experiment I investigated the role of prediction
in perception using reaching movements.
Here, I found that perceived naturalness of the predictions is
similar to the perceived naturalness of movements itself obtained
in the first experiment.
I have found that MP models are able to produce movement that looks natural,
with the TMP achieving the highest perceptual scores as well
highest predictiveness of perceived naturalness among the three
model classes, suggesting their suitability for a shared representation
of perception and action. |
|---|---|
| Physical Description: | 108 Pages |
| DOI: | 10.17192/z2021.0225 |