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<!DOCTYPE html>
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content="Articulated Gaussian splatting for animatable humans from monocular RGB videos">
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<title>SplatArmor: Articulated Gaussian splatting for animatable humans from monocular RGB videos</title>
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<h1 class="title is-1 publication-title">SplatArmor: Articulated Gaussian splatting for animatable humans from monocular RGB videos</h1>
<div class="is-size-5 publication-authors">
<span class="author-block"><a href="https://jenaroh.it" target="_blank" rel="noopener noreferrer">Rohit Jena</a><sup>1</sup></span>
<span class="author-block"><a href="" target="_blank" rel="noopener noreferrer">Ganesh Iyer</a><sup>2</sup></span>
<span class="author-block"><a href="" target="_blank" rel="noopener noreferrer">Siddharth Choudhary</a><sup>2</sup></span>
<span class="author-block"><a href="" target="_blank" rel="noopener noreferrer">Brandon M. Smith</a><sup>2</sup></span>
<span class="author-block"><a href="https://pratikac.github.io/" target="_blank" rel="noopener noreferrer">Pratik Chaudhari</a><sup>1</sup></span>
<span class="author-block"><a href="https://www.med.upenn.edu/apps/faculty/index.php/g5455356/p10656" target="_blank" rel="noopener noreferrer">James Gee</a><sup>1</sup></span>
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<span class="author-block"><sup>1</sup>University of Pennsylvania,</span>
<span class="author-block"><sup>2</sup>Amazon Inc.</span>
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<h2 class="title is-3">Abstract</h2>
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<p>
We propose SplatArmor, a novel approach for recovering detailed and animatable human models by 'armoring' a parameterized body model with 3D Gaussians. Our approach represents the human as a set of 3D Gaussians within a canonical space, whose articulation is defined by extending the skinning of the underlying SMPL geometry to arbitrary locations in the canonical space. To account for pose-dependent effects, we introduce a SE(3) field, which allows us to capture both the location and anisotropy of the Gaussians. Furthermore, we propose the use of a neural color field to provide color regularization and 3D supervision for the precise positioning of these Gaussians. We show that Gaussian splatting provides an interesting alternative to neural rendering based methods by leverging a rasterization primitive without facing any of the non-differentiability and optimization challenges typically faced in such approaches. The rasterization paradigms allows us to leverage forward skinning, and does not suffer from the ambiguities associated with inverse skinning and warping. We show compelling results on the ZJU MoCap and People Snapshot datasets, which underscore the effectiveness of our method for controllable human synthesis.
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<p>Rendering held-out test frames from the ZJU MoCap dataset (with test-time optimization of SMPL parameters).</p>
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<p><b>Left to right:</b> NeuralBody, AnimatableNeRF, SANeRF, HumanNeRF, Ours, Ground truth.</p>
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<p>Rendering from novel pose sequences selected from the AMASS dataset.</p>
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<video poster="" id="377-walkdog" autoplay controls muted loop playsinline height="100%">
<source src="./static/videos/377-walkdog.mp4"
type="video/mp4">
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<p><b>In row-major order</b>: Pose sequence, NeuralBody, AnimatableNeRF, SANeRF, HumanNeRF, Ours.</p>
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</div>
</section>
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<h2 class="title">Citation</h2>
<pre><code>@article{jena2023splatarmor,
title={SplatArmor: Articulated Gaussian splatting for animatable humans from monocular RGB videos},
author={Jena, Rohit and Iyer, Ganesh Subramanian and Choudhary, Siddharth and Smith, Brandon and Chaudhari, Pratik and Gee, James},
journal={arXiv preprint arXiv:2311.10812},
year={2023}
}</code></pre>
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