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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>TrackingWorld: World-centric Monocular 3D Tracking</title>
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</head>
<body>
<header>
<div class="container">
<h1>TrackingWorld: World-centric Monocular 3D Tracking of Almost All Pixels</h1>
<div class="badge-links">
<a href="https://arxiv.org/abs/2512.08358" target="_blank" style="background-color: #b31b1b; color: white;">
<span style="font-weight: bold;">arXiv</span>: 2512.08358
</a>
<a href="https://openreview.net/pdf?id=vDV912fa3t" target="_blank" style="background-color: #007bff; color: white;">
<span style="font-weight: bold;">NeurIPS 2025</span>
</a>
<a href="https://github.com/IGL-HKUST/TrackingWorld" target="_blank" style="background-color: #6c757d; color: white;">
<span style="font-weight: bold;">Code</span>
</a>
</div>
</div>
</header>
<div class="container">
<section class="author-section">
<h2>👥 Authors and Affiliations</h2>
<div class="author-list">
<span class="author-name"><a href="https://github.com/jiah-cloud" target="_blank">Jiahao Lu</a><sup>1</sup></span>,
<span class="author-name"><a href="https://openreview.net/profile?id=~Weitao_Xiong1" target="_blank">Weitao Xiong</a><sup>1,5</sup></span>,
<span class="author-name"><a href="https://scholar.google.com/citations?user=-0y0FpkAAAAJ&hl=zh-CN" target="_blank">Jiacheng Deng</a><sup>2</sup></span>,
<span class="author-name"><a href="https://scholar.google.com/citations?user=8eTLCkwAAAAJ&hl=zh-CN" target="_blank">Peng Li</a><sup>1</sup></span>,
<span class="author-name"><a href="https://scholar.google.com/citations?user=nhbSplwAAAAJ&hl=en" target="_blank">Tianyu Huang</a><sup>3</sup></span>,
<span class="author-name"><a href="https://frank-zy-dou.github.io/" target="_blank">Zhiyang Dou</a><sup>4</sup></span>,
<span class="author-name"><a href="https://clinplayer.github.io/" target="_blank">Cheng Lin</a><sup>6</sup></span>,
<span class="author-name"><a href="https://saikit.org/index.html" target="_blank">Sai-Kit Yeung</a><sup>1</sup></span>,
<span class="author-name"><a href="https://liuyuan-pal.github.io/" target="_blank">Yuan Liu</a><sup>1†</sup></span>
</div>
<div class="affiliations">
<p>
<sup>1</sup> The Hong Kong University of Science and Technology
<sup>2</sup> University of Science and Technology of China
</p>
<p>
<sup>3</sup> The Chinese University of Hong Kong
<sup>4</sup> The University of Hong Kong
<sup>5</sup> Xiamen University
</p>
<p>
<sup>6</sup> Macau University of Science and Technology
</p>
</div>
</section>
<hr/>
<section class="abstract-section">
<h2>📝 Abstract</h2>
<div class="abstract-content">
<p>
<strong>TrackingWorld</strong> is a novel approach for
<span style="color: #007bff; font-weight: bold;">dense, world-centric 3D tracking</span> from
<span style="color: #007bff; font-weight: bold;">monocular videos</span>. Our method estimates accurate camera poses and disentangles 3D trajectories of both static and dynamic components — not limited to a single foreground object. It supports dense tracking of nearly all pixels, enabling robust 3D scene understanding from monocular inputs.
</p>
</div>
<div class="key-features">
<div class="feature-box">
<h3>🌍 World-Centric Pose</h3>
<p>Estimates accurate camera poses for consistent 3D world coordinate system anchoring.</p>
</div>
<div class="feature-box">
<h3>🔄 Disentangled Trajectories</h3>
<p>Separates 3D motion for static background and dynamic foreground components.</p>
</div>
<div class="feature-box">
<h3>👀 Dense Pixel Coverage</h3>
<p>Supports tracking of nearly all pixels, moving beyond sparse keypoints.</p>
</div>
</div>
</section>
<hr/>
<section class="pipeline-section">
<h2>🧩 Pipeline and Methodology</h2>
<h3>Overview</h3>
<ul style="line-height: 1.7; padding-left: 18px;">
<li>
<strong>Dense 2D Trajectory Generation</strong><br>
From a monocular video sequence, <strong>TrackingWorld</strong> first extracts dense 2D point trajectories
that track both static background structures and newly emerging dynamic objects.
</li>
<li>
<strong>Optimization-based 4D Reconstruction</strong><br>
The 2D trajectories are lifted into a world-centric 3D space through a unified optimization framework consisting of:
<ul style="margin-top: 8px; line-height: 1.6; padding-left: 20px;">
<li>
<strong>Initial Camera Pose Estimation</strong><br>
We estimate coarse camera poses at the clip level, providing an essential initialization
for subsequent refinement and 3D trajectory lifting.
</li>
<li>
<strong>Dynamic Background Refinement</strong><br>
Potentially dynamic regions are identified and filtered out, enabling accurate
pose optimization using stable background correspondences.
</li>
<li>
<strong>World-centric 3D Trajectory Reconstruction</strong><br>
With refined poses and dense correspondences, we reconstruct
<strong>dense 3D trajectories</strong> that capture the motion of both static scene elements
and dynamic objects.
</li>
</ul>
</li>
</ul>
<div style="text-align: center; margin: 25px 0;">
<img src="assets/framework.png" alt="TrackingWorld Pipeline" style="max-width: 90%; border-radius: 12px; box-shadow: 0 4px 12px rgba(0,0,0,0.12);" />
<p style="font-size: 0.9rem; color: #666; margin-top: 8px;">
Figure: Overview of TrackingWorld Framework.
</p>
</div>
</section>
<hr/>
<section class="teaser-section">
<h2 style="color: #0056b3;">
🎥 Teaser and Main Results
</h2>
<p class="teaser-description">
TrackingWorld provides dense, world-centric 3D trajectories for almost every visible pixel,
enabling complete 4D scene reconstruction with high accuracy and robustness.
</p>
<div class="teaser-gif-container">
<img src="assets/TrackingWorld_video.gif"
alt="TrackingWorld 3D Output Comparison" />
<p class="figure-caption">
Figure: TrackingWorld 3D reconstruction and 2D/3D tracking demonstration.
</p>
</div>
<hr style="border-top: 1px dashed #ccc; margin: 40px 0;"/>
<div class="viz-tabs">
<h3 style="text-align: center; color: #333; margin-bottom: 25px; font-weight: 600;">
💻 Interactive 3D Demos (Click to select scene)
</h3>
<div class="tab-labels" id="scene-selector">
<button class="tab-button" data-scene="libby">Libby</button>
<button class="tab-button" data-scene="dog">Dog</button>
<button class="tab-button" data-scene="car">Car Shadow</button>
<button class="tab-button" data-scene="rollarblade">Rollarblade</button>
<button class="tab-button" data-scene="bmx">BMX Trees</button>
</div>
<div id="iframe-container">
</div>
</div>
</section>
<hr/>
<section>
<h2>📄 Citation</h2>
<p>If you find <strong>TrackingWorld</strong> useful for your research or applications, please consider citing our paper:</p>
<div class="citation-block">
<pre>@inproceedings{
lu2025trackingworld,
title={TrackingWorld: World-centric Monocular 3D Tracking of Almost All Pixels},
author={Jiahao Lu and Weitao Xiong and Jiacheng Deng and Peng Li and Tianyu Huang and Zhiyang Dou and Cheng Lin and Sai-Kit Yeung and Yuan Liu},
booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems},
year={2025},
url={https://openreview.net/forum?id=vDV912fa3t}
}</pre>
</div>
</section>
</div>
<footer>
© 2025 TrackingWorld Authors | Accepted to NeurIPS 2025
</footer>
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