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<h1 class="title is-1 publication-title">BoDiffusion: Diffusing Sparse Observations for Full-Body Human Motion Synthesis</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://angelacast135.github.io">Angela Castillo</a><sup>*1</sup>,</span>
<span class="author-block">
<a href="https://mc-escobar11.github.io">Maria Escobar</a><sup>*1</sup>,</span>
<span class="author-block">
<a href="https://guillaumejs2403.github.io">Guillaume Jeanneret</a><sup>2</sup>,
</span>
<span class="author-block">
<a href="https://www.albertpumarola.com">Albert Pumarola</a><sup>3</sup>,
</span>
<p></p>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=k0nZO90AAAAJ&hl=en&oi=ao">Pablo Arbeláez</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=7T0CPEkAAAAJ&hl=en">Ali Thabet</a><sup>3</sup>,
</span>
<span class="author-block">
<a href="https://gdude.de">Artsiom Sanakoyeu</a><sup>3</sup>
</span>
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<span class="author-block"><sup>1</sup>CINFONIA, Universidad de Los Andes, </span>
<span class="author-block"><sup>2</sup>University of Caen Normandie, ENSICAEN, CNRS, France, </span>
<p></p>
<span class="author-block"><sup>3</sup>Meta AI</span>
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<h2 class="subtitle has-text-centered">
<span class="dnerf">BoDiffusion</span> synthesizes more accurate motions with substantially less jitter than AvatarPoser.
</h2>
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<h2 class="title is-3">Abstract</h2>
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<p>
Mixed reality applications require tracking the user's full-body motion to enable an
immersive experience. However, typical head-mounted devices can only track head
and hand movements, leading to a limited reconstruction of full-body motion due
to variability in lower body configurations.
</p>
<p>
We propose <span class="dnerf">BoDiffusion</span> -- a generative diffusion model for motion synthesis to
tackle this under-constrained reconstruction problem. We present a time and
space conditioning scheme that allows <span class="dnerf">BoDiffusion</span> to leverage sparse tracking
inputs while generating smooth and realistic full-body motion sequences.
To the best of our knowledge, this is the first approach that uses the reverse
diffusion process to model full-body tracking as a conditional sequence generation task.
We conduct experiments on the large-scale motion-capture dataset AMASS and show
that our approach outperforms the state-of-the-art by a significant margin in
terms of full-body motion realism and joint reconstruction error.
</p>
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<h2 class="title is-3"><span class="dnerf">BoDiffusion</span> </h2>
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<h3 class="title is-4">Architecture</h3>
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<p>
<span class="dnerf">BoDiffusion</span> is a diffusion process synthesizing
full-body motion using sparse tracking signals as conditioning.
</p>
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<img src="./static/images/Bodiffusion.png"
class="interpolation-image"
alt="Interpolate start reference image."/>
</div>
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<h3 class="title is-4">Denoising Steps</h3>
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<p>
During inference, we start from random
Gaussian noise and perform T denoising steps until we reach a clean
output motion.
</p>
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class="interpolation-image"
alt="Interpolate start reference image."/>
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<p>
<span class="dnerf">BoDiffusion</span> synthesizes substantially more accurate
and plausible full-body poses, particularly in the lower body where no
IMU data are captured.
</p>
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<source src="./static/videos/matting.mp4"
type="video/mp4">
</video> -->
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class="interpolation-image"
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<h2 class="title is-3">More Examples</h2>
<!-- Interpolating. -->
<h3 class="title is-4">Unconventional Poses</h3>
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<p>
<span class="dnerf">BoDiffusion</span> predicts plausible poses even for uncommon
movements like crouching or lying down.
</p>
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<h3 class="title is-4">Error on individual poses</h3>
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<p>
<span class="dnerf">BoDiffusion</span> predicts poses with higher fidelity
to the ground truth. In contrast, AvatarPoser struggles
to predict accurate lower-body configurations.
</p>
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<h2 class="title is-3">Related Links</h2>
<div class="content has-text-justified">
<p>
There's a lot of excellent work that was introduced around the same time as ours.
</p>
<p>
<a href="https://arxiv.org/abs/2104.09125">Progressive Encoding for Neural Optimization</a> introduces an idea similar to our windowed position encoding for coarse-to-fine optimization.
</p>
<p>
<a href="https://www.albertpumarola.com/research/D-NeRF/index.html">D-NeRF</a> and <a href="https://gvv.mpi-inf.mpg.de/projects/nonrigid_nerf/">NR-NeRF</a>
both use deformation fields to model non-rigid scenes.
</p>
<p>
Some works model videos with a NeRF by directly modulating the density, such as <a href="https://video-nerf.github.io/">Video-NeRF</a>, <a href="https://www.cs.cornell.edu/~zl548/NSFF/">NSFF</a>, and <a href="https://neural-3d-video.github.io/">DyNeRF</a>
</p>
<p>
There are probably many more by the time you are reading this. Check out <a href="https://dellaert.github.io/NeRF/">Frank Dellart's survey on recent NeRF papers</a>, and <a href="https://github.com/yenchenlin/awesome-NeRF">Yen-Chen Lin's curated list of NeRF papers</a>.
</p>
</div>
</div>
</div> -->
<!--/ Concurrent Work. -->
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</section>
<section class="section" id="BibTeX">
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<h2 class="title">BibTeX</h2>
<pre><code>@article{castillo2023bodiffusion,
author = {Castillo, Angela and Escobar, Maria and Jeanneret, Guillaume and Pumarola, Albert and Arbeláez, Pablo and Thabet, Ali and Sanakoyeu, Artsiom},
title = {BoDiffusion: Diffusing Sparse Observations for Full-Body Human Motion Synthesis},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision},
year = {2023},
}</code></pre>
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