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<h1 class="title is-1 publication-title">EgoCast: Forecasting Egocentric Human Pose in the Wild</h1>
<div class="is-size-5 publication-authors">
<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://scholar.google.com/citations?user=1FvyvPcAAAAJ&hl=en&oi=ao">Juanita Puentes</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=zRFjbnYAAAAJ&hl=en&oi=ao">Cristhian Forigua</a><sup>1</sup>,
</span>
<p></p>
<span class="author-block">
<a href="https://jponttuset.cat">Jordi Pont-Tuset</a><sup>2</sup>,
</span>
<span class="author-block">
<a href="https://www.kmaninis.com">Kevis-Kokitsi Maninis</a><sup>2</sup>,
</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=k0nZO90AAAAJ&hl=en&oi=ao">Pablo Arbeláez</a><sup>1</sup>,
</span>
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<span class="author-block"><sup>1</sup>CINFONIA, Universidad de Los Andes, Bogotá </span>
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<span class="author-block"><sup>2</sup>Google, Zürich </span>
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<h2 class="subtitle has-text-centered">
<span class="dnerf">EgoCast</span> is a novel framework for full-body pose forecasting. We use visual and proprioceptive cues to accurately predict body motion.
</h2>
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<h2 class="title is-3">Abstract</h2>
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<p>
Accurately estimating and forecasting human body pose is important for enhancing the user's sense of immersion in Augmented
Reality. Addressing this need, our paper introduces <span class="dnerf">EgoCast</span>, a bimodal method for 3D human pose forecasting using egocentric
videos and proprioceptive data. We study the task of human pose forecasting in a realistic setting, extending the boundaries
of temporal forecasting in dynamic scenes and building on the current framework for current pose estimation in the wild.
We introduce a current-frame estimation module that generates pseudo-groundtruth poses for inference, eliminating the
need for past groundtruth poses typically required by current methods during forecasting. Our experimental results on
the recent Ego-Exo4D and Aria Digital Twin datasets validate <span class="dnerf">EgoCast</span> for real-life motion estimation. On the Ego-Exo4D
Body Pose 2024 Challenge, our method significantly outperforms the state-of-the-art approaches, laying the groundwork
for future research in human pose estimation and forecasting in unscripted activities with egocentric inputs.
</p>
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<h2 class="title is-3"><span class="dnerf">EgoCast</span> </h2>
<!-- Interpolating. -->
<h3 class="title is-4">Human Pose Forecasting Benchmark</h3>
<div class="column content">
<p>
The task for the Human Pose Forecasting Benchmark is predicting a set of 3D human poses in the future given visual and proprioceptive data from the past.
EgoCast focuses on a realistic forecasting setting since it does not assume that the models will have
access to ground-truth poses from previous frames, and we evaluate on longer than usual timeframes.
</p>
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alt="Benchmark formulation."/>
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<h3 class="title is-4">Architecture</h3>
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<p>
Given a sequence of camera poses and RGB images from the headset, we first estimate the 3D full-body pose at each timestamp via the current-frame estimation module.
Then, the pose forecasting module uses these predicted body poses, together with the proprioceptive inputs, to estimate the 3D human poses in the following frames in the future.
Overall, Egocast proposes a whole pipeline for 3D human pose estimation in real-world scenarios where only the input streams given by the headset are available.
</p>
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class="interpolation-image"
alt="Interpolate start reference image."/>
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<h3 class="title is-4">Results</h3>
<div class="column content">
<h3 class="title is-5">Current-frame pose estimation</h3>
<div class="content has-text-centered">
<img src="./static/images/joints_V2.png"
class="interpolation-image"
alt="Interpolate start reference image."/>
</div>
<h3 class="title is-5">Pose forecasting</h3>
<div class="content has-text-centered">
<img src="./static/images/qual2.png"
class="interpolation-image"
alt="Interpolate start reference image."/>
</div>
</div>
</div>
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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>.
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<section class="section" id="BibTeX">
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<h2 class="title">BibTeX</h2>
<pre><code>@article{escobar2025egocast,
author = {Escobar, Maria and Puentes, Juanita and Forigua, Cristhian and Pont-Tuset, Jordi and Maninis, Kevis-Kokitsi and Arbeláez, Pablo},
title = {EgoCast: Forecasting Egocentric Human Pose in the Wild},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
year = {2025},
}</code></pre>
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