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animate3d committed Sep 26, 2024
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80 changes: 74 additions & 6 deletions index.html
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</video>
<div class="title-content">
<h1>Animate3D: Animating Any 3D Model with Multi-view Video Diffusion</h1>
<p class="authors">
<span class="author-name">Yanqin Jiang<sup>1*</sup>,</span>
<span class="author-name">Chaohui Yu,<sup>2*</sup></span>
<span class="author-name">Chenjie Cao<sup>2</sup>,</span>
<span class="author-name">Fan Wang<sup>2</sup>,</span>
<span class="author-name">Weiming Hu<sup>1</sup>,</span>
<span class="author-name">Jin Gao<sup>1</sup></span>
</p>
<p class="institute"> <sup>1</sup>CASIA<br>
<sup>2</sup>DAMO Academy, Alibaba Group</p>
<p class="accept">NeurIPS 2024</p>
<div class="links">
<a href="https://arxiv.org/abs/2407.11398" target="_blank">
<i class="fas fa-file-pdf icon"></i> Arxiv
</a>
<a href="https://youtu.be/qkaeeGzLnY8" target="_blank">
<i class="fas fa-video icon"></i> Video
</a>
<a href="[Data链接]" target="_blank">
<i class="fas fa-database icon"></i> Data
</a>
<a href="https://github.com/yanqinJiang/Animate3D" target="_blank">
<i class="fab fa-github icon"></i> Code
</a>
</div>
</div>
</section>

Expand All @@ -36,13 +61,33 @@ <h2>Abstract</h2>
Specifically, for MV-VDM, we design a new spatiotemporal attention module to enhance spatial and temporal consistency by integrating 3D and video diffusion models.
Additionally, we leverage the static 3D model's multi-view renderings as conditions to preserve its identity.
For animating 3D models, an effective two-stage pipeline is proposed: we first reconstruct motions directly from generated multi-view videos, followed by the introduced 4D-SDS to refine both appearance and motion.
Benefiting from accurate motion learning, we could achieve straightforward mesh animation.
Qualitative and quantitative experiments demonstrate that Animate3D significantly outperforms previous approaches.
Data, code, and models will be open-released.
</p>
</div>
</section>

<!-- Section 3: Animating 3D Mesh -->
<!-- Section 3: Video, 16:9 Youtube视频 -->
<section id="youtube-video">
<h2>Video</h2>
<div class="content-center">
<p>The video is best viewed in <span class="highlight"> 4K mode</span>.</p>
</div>
<div class="video-container">
<div class="video-wrapper-16-9">
<iframe
src="https://www.youtube.com/embed/qkaeeGzLnY8?si=ahBAiCBjfeKLeptj"
title="YouTube video player"
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
referrerpolicy="strict-origin-when-cross-origin"
allowfullscreen>
</iframe>
</div>
</div>
</section>

<!-- Section 4: Animating 3D Mesh -->
<section id="animate-generated">
<h2>Animate Generated 3D Mesh</h2>
<div class="content-center">
Expand All @@ -69,7 +114,7 @@ <h2>Animate Generated 3D Mesh</h2>
</div>
</section>

<!-- Section 4: Animating Reconstructed 3D Model -->
<!-- Section 5: Animating Reconstructed 3D Model -->
<section id="animate-reconstructed">
<h2>Animate Reconstructed 3D Model</h2>
<div class="content-center">
Expand All @@ -95,7 +140,7 @@ <h2>Animate Reconstructed 3D Model</h2>
</div>
</section>

<!-- Section 5: Animating Real-world 3D Scan -->
<!-- Section 6: Animating Real-world 3D Scan -->
<section id="animate-real-world">
<h2>Animate Real-world 3D Scan</h2>
<div class="content-center">
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</div>
</section>

<!-- Section 6: Animating Generated 3D Model -->
<!-- Section 7: Animating Generated 3D Model -->
<section id="animate-generated">
<h2>Animate Generated 3D Model</h2>
<div class="content-center">
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</div>
</section>

<!-- Section 7: Other Section -->
<!-- Section 8: Other Section -->
<section id="other">
<h2>Ablation for 4D-SDS</h2>
<div class="content-center">
Expand All @@ -169,13 +214,36 @@ <h2>Ablation for 4D-SDS</h2>
</div>
</section>

<!-- Section 8: Our Training Data -->
<section id="training-data">
<h2>Training Data</h2>
<div class="content-center">
<p>
Our training dataset, <span class="highlight">MV-Video</span>, comprises <span class="highlight">115K animations</span> that are available under a public license, consisting of about <span class="highlight">53K animated 3D objects</span> at all,
which are rendered into over <span class="highlight">1.8M multi-view videos</span>. <br>
Notably, our training data is manually selected and with <span class="highlight">high-quality</span>. It includes <span class="highlight">the highest quality part of <a href="https://objaverse.allenai.org/objaverse-1.0/" target="_blank">Objaverse</a> (around 7K animated 3D objects)</span>, while the rest <span class="highlight">(around 46K animated 3D objects)</span> are collected by ourselves.
</p>
</div>
</section>

<!-- Section 9: Relevant Works -->
<section id="relevant-work">
<h2>Relevant Works</h2>
<div class="content-center">
<p>
[1] <a href="https://sc4d.github.io/" target="_blank">SC4D: Sparse-Controlled Video-to-4D Generation and Motion Transfer</a> (ECCV 2024) <br>
[2] <a href="https://nju-3dv.github.io/projects/STAG4D/" target="_blank">STAG4D: Spatial-Temporal Anchored Generative 4D Gaussians</a> (ECCV 2024) <br>
[3] <a href="https://consistent4d.github.io/" target="_blank">Consistent4D: Consistent 360° Dynamic Object Generation from Monocular Video</a> (ICLR 2024)
</p>
</div>
</section>

<!-- Section 10: Acknowledgements -->
<section id="acknowledgements">
<h2>Acknowledgements</h2>
<div class="content-center">
<p>Some 3D assets for animation are downloaded from <span class="highlight"><a href="https://sketchfab.com/" target="_blank">sketchfab</a></span>, under <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">CC Attribution</a> and <a href="https://creativecommons.org/licenses/by-nc/4.0/" target="_blank">CC Attribution-NonCommercial</a>.
We would like to thank <span class="highlight"><a href="">the creators</a></span>for sharing great 3D assets.
We would like to thank <span class="highlight"><a href="assets/acknowledgements.txt">the creators</a></span>for sharing great 3D assets.
</p>
</div>
</section>
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margin: 5px 0;
}

.accept {
font-size: 1.6rem;
color: #ffffff;
text-align: center;
margin: 5px 0;
}

/* 链接文本的样式 */
.links {
text-align: center;
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