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Gazeal committed Sep 28, 2024
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21 changes: 21 additions & 0 deletions LICENSE
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MIT License

Copyright (c) 2023 AIOZ AI

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
8 changes: 6 additions & 2 deletions README.md
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# VAE
Scalable Group Choreography via Variational Phase Manifold Learning
# Scalable Group Choreography via Variational Phase Manifold Learning

## Code
<i>Coming soon...</i>

🌟 Stay tuned for more information and join us at ECCV 2024.
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<!DOCTYPE html>
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<head>
<meta charset="utf-8">
<meta name="description"
content="GDANCE, a new large-scale dataset for music-driven group dance generation.">
<meta name="keywords" content="GDANCE, Gdance, AIOZ, AIOZ AI">
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<h1 class="title is-1 publication-title">Scalable Group Choreography via Variational Phase Manifold Learning</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://minhnhatvt.github.io/">Nhat Le</a><sup>1</sup>,</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=qCcSKkMAAAAJ&hl=en">Tuong Do</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="">Khoa Do</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="">Xuan Bui</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://sg.linkedin.com/in/erman-tjiputra">Erman Tjiputra</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=DbAThEgAAAAJ&hl=en">Quang D. Tran</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://cgi.csc.liv.ac.uk/~anguyen/">Anh Nguyen</a><sup>2</sup>,
</span>
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<!--/ Intro Image. -->
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>1</sup><b>AIOZ, Singapore</b></span><br />
<span class="author-block"><sup>2</sup><b>VNUHCM-University of Science, Vietnam</b></span><br />
<span class="author-block"><sup>3</sup><b>University of Liverpool, UK</b></span>
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<i class="ai ai-arxiv"></i>
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<span>arXiv</span>
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<h2 class="title is-3">Abstract</h2>
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Generating group dance motion from the music is a challenging task with several industrial applications. Although several methods have been proposed to tackle this problem, most of them prioritize optimizing the fidelity in dancing movement, constrained by predetermined dancer counts in datasets. This limitation impedes adaptability to real-world applications. Our study addresses the scalability problem in group choreography while preserving naturalness and synchronization. In particular, we propose a phase-based variational generative model for group dance generation on learning a generative manifold. Our method achieves high-fidelity group dance motion and enables the generation with an unlimited number of dancers while consuming only a minimal and constant amount of memory. The intensive experiments on two public datasets show that our proposed method outperforms recent state-of-the-art approaches by a large margin and is scalable to a great number of dancers beyond the training data.
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<h2 class="title">Method</h2>
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In this paper, our goal is to develop a scalable technique for group dance generation, a phase-based variational generative model for scalable group dance generation, namely Phase-conditioned Dance VAE (PDVAE). To our knowledge, PDVAE is the first method to represent the variational latent space using phase parameters in the frequency domain of the motion curves. Our method goes beyond the conventional VAE approach that typically relies on a single latent vector drawn from a Gaussian distribution, which is unable to adequately represent the temporal information of the motion sequence (e.g., the time dimension is squeezed out).
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<section class="section" id="BibTeX">
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<h2 class="title">BibTeX</h2>
<pre><code>Soon
</code></pre>
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The website template was borrowed from <a
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