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<!DOCTYPE html>
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<meta name="description"
content="Motion2Language mapping and time synchronization through local reccurrent attention">
<meta name="keywords" content="Motion segmentation, Local recurrent attention, Synchronization, Unsupervised learning">
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<title>Motion2language, unsupervised learning of synchronized semantic motion segmentation</title>
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<h1 class="title is-1 publication-title">Motion2language, unsupervised learning of <span style="color: goldenrod;">synchronized</span> semantic motion segmentation</h1>
<div class="is-size-4 publication-authors">
<span class="author-block">
<a href="">Karim Radouane</a>,</span>
<span class="author-block">
<a href="">Andon Tchechmedjiev</a>,</span>
<span class="author-block">
<a href="">Julien Lagarde</a>,
</span>
<span class="author-block">
<a href="">Sylvie Ranwez</a>
</span>
</div>
<br/>
<div class="is-size-4 publication-authors">
<span class="author-block">EuroMovDHM, University of Montpellier, IMT Mines Ales</span>
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<h2 class="title is-3">Abstract</h2>
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<p>
In this paper, we investigate building a sequence to sequence architecture for motion to language translation and synchronization.
The aim is to translate motion capture inputs into English natural-language descriptions, such that the descriptions are generated synchronously with the actions performed, enabling semantic segmentation as a byproduct, but without requiring synchronized training data.
</p>
<p>
We propose a new recurrent formulation of local attention that is suited for synchronous text generation, as well as an improved motion encoder architecture better suited to smaller data and for synchronous generation.
We evaluate both contributions in individual experiments, using the standard BLEU4 metric, as well as a simple semantic equivalence measure, on the KIT motion language dataset.
In a follow-up experiment, we assess the quality of the synchronization of generated text in our proposed approaches through multiple evaluation metrics.
We find that both contributions to the attention mechanism and the encoder architecture additively improve the quality of generated text (BLEU and semantic equivalence), but also of synchronization.
</p>
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<h2 class="title is-3">Local Recurrent Attention</h2>
<p>
Using <i>local recurrent attention</i> weights we can derive synchronization information between motion and words.
</p>
<ul>
<li style="font-weight: bold;">Compositional motion</li>
</ul>
<img src="./static/images/local_recurrent_attention.PNG"
class="paper-image"
alt=""/>
<br/>
<ul>
<li style="font-weight: bold;">Single action</li>
</ul>
<img src="./static/images/stomp_loc_rec_att.PNG"
class="paper-image"
alt=""/>
<br/>
</div>
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<div class="column">
<h2 class="title is-3">Motion Frozen in Time</h2>
<div class="columns is-centered">
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<p>
We can use attention weights as transparency level of motion frames and better visualize synchronization.
</p>
<br/>
<img src="./static/images/frozen_motions.png"
class="paper-image"
alt=""/>
</div>
</div>
</div>
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<h2 class="title is-3"> Graphical abstract</h2>
<img src="./static/images/approach.jpg"
class="paper-image"
alt=""/>
</div>
</div>
<br/>
<!--/ Comparison. -->
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<h2 class="title is-3"> Comparison</h2>
<p>The novelty of our approach stems from the ability to infer local mapping
between motion primitives and their subtitles using local recurrent attention enabling for the first time a synchronized captioning.</p>
<br/>
<br/>
<img src="./static/images/comparison_approach.PNG"
class="paper-image"
alt=""/>
</div>
</div>
<!--/ Comparison. -->
<h2 class="title is-3">Segmentation process</h2>
<p>
For quantitative evaluation we define motion and language segments and use metrics such as IoU.</p>
<br>
<img src="./static/images/lang_motion_seg.PNG"
class="paper-image"
alt=""/>
</div>
</div>
<!--/ Methods. -->
</div>
</section>
<section class="section" id="BibTeX">
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<h2 class="title">BibTeX</h2>
<pre><code>@article{radouane23motion2language,
author={Radouane, Karim and Tchechmedjiev, Andon and Lagarde, Julien and Ranwez, Sylvie},
title={Motion2language, unsupervised learning of synchronized semantic motion segmentation},
journal={Neural Computing and Applications},
ISSN={1433-3058},
url={http://dx.doi.org/10.1007/s00521-023-09227-z},
DOI={10.1007/s00521-023-09227-z},
publisher={Springer Science and Business Media LLC},
year={2023},
month=dec}
</code></pre>
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