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---
permalink: /
multiview: [3194, 3180, 3131, 3047, 3099, 3039, 3134, 3084, 3128, 3214, 3115, 3064, 3106, 3231, 3072, 3132, 3157, 3079, 3138, 3029, 3046]
pose_interp: [9068, 9075, 9107, 9112, 9118, 9132, 9145]
---
<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<meta name="description" content="3DHumanGAN: 3D-Aware Human Image Generation with 3D Pose Mapping">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>3DHumanGAN: 3D-Aware Human Image Generation with 3D Pose Mapping</title>
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<script src="/assets/js/index.js"></script>
</head>
<body>
<div class="box" id="loading">
<div class="container" style="padding: 20px;">
<div class="columns is-centered">
<h5 class="title is-5" style="margin-bottom: 15px;">Loading ...</h5>
</div>
<div class="columns is-centered">
<progress class="progress is-large is-info" max="100">60%</progress>
</div>
</div>
</div>
<br>
<section class="hero">
<div class="hg3d-body">
<div class="container is-max-desktop">
<div class="columns is-centered">
<div class="column has-text-centered">
<h3 class="title is-2 publication-title">3DHumanGAN: 3D-Aware Human Image Generation with 3D Pose Mapping</h3>
<div class="is-size-5 publication-authors">
<span class="author-block">
<h3 style="color:#5a6268;">ICCV 2023</h3>
<span class="author-block">
<a href="https://yzhq97@github.io">Zhuoqian Yang</a><sup>1,2</sup>, </span>
<span class="author-block">
<a href="mailto:lishikai@pjlab.org.cn">Shikai Li</a><sup>1</sup>, </span>
<!-- <span class="author-block">
<a href="mailto:wangxiaogang@sensetime.com">Xiaogang Wang</a><sup>3</sup>, </span> -->
<span class="author-block">
<a href="https://wywu.github.io/">Wayne Wu</a><sup>1† </sup>,
</span>
<span class="author-block">
<a href="https://daibo.info/">Bo Dai</a><sup>1</sup>
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>1</sup>Shanghai AI Laboratory, </span>
<span class="author-block"><sup>2</sup>EPFL</span>
<!-- <span class="author-block"><sup>3</sup>CUHK</span> -->
</div>
<div class="column has-text-centered">
<div class="publication-links">
<span class="link-block">
<a href="https://arxiv.org/abs/2212.07378"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="ai ai-arxiv"></i>
</span>
<span>Arxiv</span>
</a>
</span>
<span class="link-block">
<a href="https://github.com/3dhumangan/3DHumanGAN"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fab fa-github"></i>
</span>
<span>GitHub</span>
</a>
</span>
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class="external-link button is-normal is-rounded is-dark">
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<span>Supplementary</span>
</a>
</span>
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<div class="column is-full">
<small style="color:#777777">Note: Please use a browser other than Safari for HTML5 videos to play properly.</small>
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</section>
<!-- Teaser -->
<!-- <section class="hero teaser">
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</video>
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</section> -->
<!-- Teaser -->
<!-- Abstract. -->
<section class="section">
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<div class="column is-four-fifths">
<h3 class="title is-3">Abstract</h3>
<div class="content has-text-justified">
<p>
We present 3DHumanGAN, a 3D-aware generative adversarial network that synthesizes photorealistic images of full-body humans with consistent appearances under different view-angles and body-poses. To tackle the representational and computational challenges in synthesizing the articulated structure of human bodies, we propose a novel generator architecture in which a 2D convolutional backbone is modulated by a 3D pose mapping network. The 3D pose mapping network is formulated as a renderable implicit function conditioned on a posed 3D human mesh. This design has several merits: i) it leverages the strength of 2D GANs to produce high-quality images; ii) it generates consistent images under varying view-angles and poses; iii) the model can incorporate the 3D human prior and enable pose conditioning.
</p>
</div>
</div>
</div>
</section>
<!--/ Abstract. -->
<!-- Paper video. -->
<section>
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-8">
<h3 class="title is-3">Video Demo</h3>
<div class="publication-video">
<iframe width="560" height="315" src="https://www.youtube.com/embed/-bUNfhNYj24?si=rI3sKFJ2sYMmim8U" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
</div>
</div>
</div>
</div>
</section>
<!-- / Paper video. -->
<section class="section">
<div class="container is-max-desktop has-text-centered">
<div class="columns is-centered">
<!-- Qualitative Results -->
<div class="column is-full has-carousel">
<h3 class="title is-4">Qualitative Results</h3>
<p>Browse through random generations.</p>
<br><br>
<div id="qr-carousel" class="carousel results-carousel">
{% for id in page.multiview %}
<div class="item card">
<video muted preload autoplay="autoplay" loop playsinline>
<source src="/assets/multiview/{{ id }}.mp4" type="video/mp4">
<p>Your browser does not support HTML5 video.</p>
</video>
</div>
{% endfor %}
</div>
</div>
<!--/ Qualitative Results -->
</div>
<div class="columns is-centered">
<div class="column is-full has-carousel">
<h3 class="title is-4">View Consistency</h3>
<p>
The appearance of the generated humans are consistent under different poses and view angles.
</p>
<br>
<div id="vc-carousel" class="carousel results-carousel">
{% for i in (1..4) %}
<div class="item card">
<video muted preload autoplay loop playsinline>
<source src="/assets/view_consistency/vc{{ i }}.mp4" type="video/mp4">
<p>Your browser does not support HTML5 video.</p>
</video>
</div>
{% endfor %}
</div>
</div>
</div>
<div class="columns is-centered">
<div class="column is-full has-carousel">
<h3 class="title is-4">Appearance Interpolation</h3>
<div class="content has-text-centered">
<p>
We can also animate the scene by interpolating the deformation latent codes of two input
frames. Use the slider here to linearly interpolate between the left frame and the right
frame.
</p>
</div>
<div class="card" style="padding: 1rem 2rem; border-radius: 1rem">
<div class="columns is-vcentered">
<div class="column is-half interpolation-video-column">
<div class="interpolation-image-wrapper" id="interpolation-image-wrapper-1">
Loading...
</div>
<div class="interpolation-image-wrapper" id="interpolation-image-wrapper-2">
Loading...
</div>
</div>
<div class="column is-half interpolation-video-column">
<div class="interpolation-image-wrapper" id="interpolation-image-wrapper-3">
Loading...
</div>
<div class="interpolation-image-wrapper" id="interpolation-image-wrapper-4">
Loading...
</div>
</div>
</div>
<div class="columns is-centered">
<div class="column is-full">
<input class="slider is-fullwidth is-large is-info"
id="interpolation-slider"
step="1" min="0" max="120" value="0" type="range">
</div>
</div>
</div>
</div>
</div>
<br><br>
<div class="columns is-centered">
<div class="column is-12">
<h3 class="title is-4">Pose Interpolation</h3>
<div class="content has-text-justified">
Our 3D-aware GAN can be used to render simple animation of generated humans by interpolating between poses.
</div>
<br><br>
<div id="pi-carousel" class="carousel results-carousel">
{% for id in page.pose_interp %}
<div class="item card">
<video muted preload autoplay loop playsinline>
<source src="/assets/pose/{{ id }}_pose.mp4" type="video/mp4">
<p>Your browser does not support HTML5 video.</p>
</video>
</div>
{% endfor %}
</div>
</div>
</div>
</div>
</section>
<br><br>
<section>
<div class="container is-max-desktop">
<!-- Concurrent Work. -->
<!-- <div class="columns is-centered">
<div class="column is-full">
<h3 class="title is-4">Concurrent Works</h3>
<div class="content has-text-justified">
<p>
These exciting works were introduced around the same time as ours.
</p>
<ul>
<li><a href="https://arxiv.org/abs/2208.00561">AvatarGen: a 3D Generative Model for Animatable Human Avatars</a></li>
</ul>
<ul>
<li><a href="https://arxiv.org/pdf/2206.14314">Generative Neural Articulated Radiance Fields</a></li>
</ul>
</div>
</div>
</div> -->
</div>
<!--/ Concurrent Work. -->
<br><br>
<div class="container is-max-desktop content">
<h3 class="title">BibTeX</h3>
<p>If you find this work useful for your research, please consider citing our paper:</p>
<pre><code>@inproceedings{yang20233dhumangan,
title={3DHumanGAN: 3D-Aware Human Image Generation with 3D Pose Mapping},
author={Yang, Zhuoqian and Li, Shikai and Wu, Wayne and Dai, Bo},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
pages={23008--23019},
year={2023}
}</code></pre>
<br><br>
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</section>
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