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<html>
<head>
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
<title>DELTA: Dense Efficient Long-range 3D Tracking for Any video</title>
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<style>
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iframe {
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width: 36px;
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}
.instruction-item_small img {
width: 30px;
height: 30px;
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</style>
</head>
<body>
<div class="content">
<h1 style="line-height: 1.2;">
<!-- <img src="./icons/demon_logo_transparent.png" alt="D3Mon Icon" style="width: 48px; vertical-align: middle;"> -->
<!-- <strong><span style="color:rgb(255, 195, 21);">DELTA:</span> Dense Long-range 3D Tracking</strong> -->
<strong>DELTA: Dense Efficient Long-range <br> 3D Tracking for Any video</strong>
</h1>
<p id="authors">
<span>
<a href="https://ngoductuanlhp.github.io//">Tuan Duc Ngo<sup>1,2</sup></a>
</span>
<span>
<a href="https://payeah.net/">Peiye Zhuang<sup>1</sup></a>
</span>
<span>
<a href="https://people.csail.mit.edu/ganchuang/">Chuang Gan<sup>2</sup></a>
</span>
<span>
<a href="https://kalo-ai.github.io/">Evangelos Kalogerakis<sup>2,3</sup></a>
</span>
<br>
<span>
<a href="http://www.stulyakov.com/">Sergey Tulyakov<sup>1</sup></a>
</span>
<span>
<a href="http://hsinyinglee.com/">Hsin-Ying Lee<sup>1</sup></a>
</span>
<a href="https://mightychaos.github.io/">Chaoyang Wang<sup>1</sup></a>
</span>
<br>
<span class="institution">
<a href="https://research.snap.com/"><sup>1</sup> Snap Inc</a>
<a href="https://www.cics.umass.edu/"><sup>2</sup> UMass Amherst</a>
<a href="https://www.ece.tuc.gr/en/home"><sup>3</sup> TU Crete</a>
</span>
</p>
<font size="+2">
<p style="text-align: center;">
<a href="files/paper.pdf" target="_blank">[Paper]</a>
<a href="https://arxiv.org/abs/2410.24211" target="_blank">[Arxiv]</a>
<a href="#" target="_blank" onclick="return false;">[Code (soon)]</a>
<a href="files/bibtex.txt" target="_blank">[BibTeX]</a>
</p>
</font>
<br>
<center>
<div class="video-container">
<video class='round' autoplay muted loop playsinline style='width: 800px' src='resources/demo_trajs/car-roundabout_traj_concat.mp4'></video>
</div>
</center>
<br>
<center>
<div class="video-container">
<video class='round' autoplay muted loop playsinline style='width: 800px' src='resources/demo_trajs/butterfly_traj_concat.mp4'></video>
</div>
</center>
<br>
<center>
<div class="video-container">
<video class='round' autoplay muted loop playsinline style='width: 800px' src='resources/demo_trajs/tortoise_swim.mp4'></video>
</div>
</center>
<br>
<center>
<div class="video-container">
<video class='round' autoplay muted loop playsinline style='width: 800px' src='resources/demo_trajs/yellow-duck.mp4'></video>
</div>
</center>
<br>
<center>
<h4></strong><strong>DELTA</strong> captures <strong>dense, long-range, 3D</strong> trajectories from casual videos in a <strong>feed-forward</strong> manner.</h4>
</center>
</div>
<!-- <div class="content">
<h2 style="text-align:left;">Intro Video (1min)</h2>
<video width="100%" controls>
<source src="./resources/video_1min.mp4" type="video/mp4">
</video>
</div> -->
<div class="content">
<h2 style="text-align:left;"><strong>Abstract</strong></h2>
<!-- <font size="-0."> -->
<p>
Tracking dense 3D motion from monocular videos remains challenging, particularly
when aiming for pixel-level precision over long sequences. We introduce DELTA,
a novel method that efficiently tracks every pixel in 3D space, enabling accurate
motion estimation across entire videos. Our approach leverages a joint
global-local attention mechanism for reduced-resolution tracking, followed by a
transformer-based upsampler to achieve high-resolution predictions. Unlike existing
methods, which are limited by computational inefficiency or sparse tracking,
DELTA delivers dense 3D tracking at scale, running over 8x faster than
previous methods while achieving state-of-the-art accuracy. Furthermore, we explore
the impact of depth representation on tracking performance and identify
log-depth as the optimal choice. Extensive experiments demonstrate the superiority
of DELTA on multiple benchmarks, achieving new state-of-the-art results in
both 2D and 3D dense tracking tasks. Our method provides a robust solution for
applications requiring fine-grained, long-term motion tracking in 3D space.
</p>
<!-- </font> -->
</div>
<div class="content">
<h2 style="text-align:left;"><strong>Motivation</strong></h2>
<img class="summary-img" src="./resources/combined.png" style="width:100%;margin-bottom: -10px;">
<br>
<p> Existing motion prediction methods struggle with short-term, sparse predictions and often fail to deliver accurate 3D motion estimations while optimization-based approaches require substantial time to process a single video.
We are the first method capable of <strong>efficiently</strong> tracking <strong>every pixel</strong> in <strong>3D space</strong> over <strong>hundreds of frames</strong> from monocular videos, and achieves
<strong>state-of-the-art accuracy</strong> on 3D tracking benchmarks.
</p>
</div>
<div class="content">
<h2 style="text-align:left;"><strong>Method</strong></h2>
<br>
<img class="summary-img" src="./resources/arch.png" style="width:100%;margin-bottom: -10px;">
<br>
<br>
<p>
DELTA takes RGB-D videos as input and achieves efficient dense
3D tracking using a coarse-to-fine strategy, beginning with coarse tracking through a <strong>spatio-temporal
attention</strong> mechanism at reduced resolution, followed by an <strong>attention-based upsampler</strong>
for high-resolution predictions.
</p>
</div>
<div class="content">
<!-- <h2 style="text-align:left;">Dense 3D Tracking in synthetic and in-the-wild videos</h2> -->
<h2 style="text-align:left;"><strong>More qualitative results</strong></h2>
<br>
<div id="myCarousel" class="carousel slide">
<!-- Indicators -->
<ol class="carousel-indicators">
<li data-target="#myCarousel" data-slide-to="0" class="active"></li>
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<li data-target="#myCarousel" data-slide-to="5"></li> -->
</ol>
<!-- Wrapper for slides -->
<div class="carousel-inner">
<!-- <div class="item active">
<table align=center width=900px>
<tr>
<video class='round' autoplay muted loop playsinline style='height:200px' src='resources/demo_trajs/tortoise_traj_concat.mp4'></video>
</tr>
<tr>
<video class='round' autoplay muted loop playsinline style='height:200px' src='resources/demo_trajs/kite-surf_traj_concat.mp4'></video>
</tr>
</table>
</div> -->
<div class="item active">
<table align=center width=900px>
<tr>
<center>
<video class='round' autoplay muted loop playsinline style='height:200px' src='resources/demo_trajs/tortoise_traj_concat.mp4'></video>
</center>
</tr>
<tr>
<center>
<video class='round' autoplay muted loop playsinline style='height:200px' src='resources/demo_trajs/kite-surf_traj_concat.mp4'></video>
</center>
</tr>
</table>
</div>
<div class="item">
<table align=center width=900px>
<tr>
<td>
<center>
<video class='round' autoplay muted loop playsinline style='height:240px' src='resources/dense_videos/input_10fps/birthcake.mp4'></video>
</center>
</td>
<td>
<center>
<video class='round' autoplay muted loop playsinline style='height:240px' src='resources/dense_videos/ours/birthcake.mp4'></video>
</center>
</td>
</tr>
<tr>
<td>
<center>
<video class='round' autoplay muted loop playsinline style='height:240px' src='resources/dense_videos/input_10fps/yellow-duck.mp4'></video>
</center>
</td>
<td>
<center>
<video class='round' autoplay muted loop playsinline style='height:240px' src='resources/dense_videos/ours/yellow-duck.mp4'></video>
</center>
</td>
</tr>
</table>
</div>
<div class="item">
<table align=center width=900px>
<tr>
<td>
<center>
<video class='round' autoplay muted loop playsinline style='height:240px' src='resources/dense_videos/input_10fps/tortoise.mp4'></video>
</center>
</td>
<td>
<center>
<video class='round' autoplay muted loop playsinline style='height:240px' src='resources/dense_videos/ours/tortoise.mp4'></video>
</center>
</td>
</tr>
<tr>
<td>
<center>
<video class='round' autoplay muted loop playsinline style='height:240px' src='resources/dense_videos/input_10fps/tortoise_swim.mp4'></video>
</center>
</td>
<td>
<center>
<video class='round' autoplay muted loop playsinline style='height:240px' src='resources/dense_videos/ours/tortoise_swim.mp4'></video>
</center>
</td>
</tr>
</table>
</div>
<div class="item">
<table align=center width=900px>
<tr>
<td>
<center>
<video class='round' autoplay muted loop playsinline style='height:240px' src='resources/dense_videos/input_10fps/dreureka_robot.mp4'></video>
</center>
</td>
<td>
<center>
<video class='round' autoplay muted loop playsinline style='height:240px' src='resources/dense_videos/ours/dreureka_robot.mp4'></video>
</center>
</td>
</tr>
<tr>
<td>
<center>
<video class='round' autoplay muted loop playsinline style='height:240px' src='resources/dense_videos/input_10fps/robot_arm.mp4'></video>
</center>
</td>
<td>
<center>
<video class='round' autoplay muted loop playsinline style='height:240px' src='resources/dense_videos/ours/robot_arm.mp4'></video>
</center>
</td>
</tr>
</table>
</div>
</div>
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<a class="left carousel-control" href="#myCarousel" data-slide="prev">
<span class="glyphicon glyphicon-chevron-left"></span>
<span class="sr-only">Previous</span>
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<span class="sr-only">Next</span>
</a>
</div>
<br>
</div>
<div class="content">
<h2 style="text-align:left;"><strong>Comparison with previous work</strong></h2>
<br>
<tr>
<center>
<video class='round' autoplay muted loop playsinline style='height:120px' src='resources/dense_videos/concat/car-roundabout_concat_compare.mp4' controls style="border: 5px solid black;"></video>
</center>
</tr>
<br>
<tr>
<center>
<video class='round' autoplay muted loop playsinline style='height:120px' src='resources/dense_videos/concat/rollerblade_concat_compare.mp4' controls style="border: 5px solid black;"></video>
</center>
</tr>
<br>
<h4 class="title is-3">
More results of 3D dense tracking can be found <a href="compare_dense.html">here</a>.
</h4>
</div>
<!-- width="800px" height="400px" src="http://localhost:8000/build/?playbackPath=http://localhost:8000/resources/viser_records/recording_car-roundabout.viser&initDistanceScale=0.3&initHeightOffset=0.1"> -->
<!-- https://rsrd-anonymous.github.io -->
<div class="content">
<h2 style="text-align:left;"><strong>Application: Non-rigid structure from motion</strong></h2>
<br>
<center>
<iframe
width="800px" height="400px" src="https://rsrd-anonymous.github.io/build/?playbackPath=https://snap-research.github.io/DELTA/resources/viser_records/recording_car-roundabout.viser&initDistanceScale=0.3&initHeightOffset=0.1">
</iframe>
</center>
<br>
We first densely track pixels across multiple keyframes in the video to obtain pairwise correspondences.
Using these correspondences, we jointly estimate per-keyframe depth maps and camera poses through the Global Alignment in
<a href="https://dust3r.europe.naverlabs.com/">DUSt3R</a> and <a href="https://monst3r-project.github.io/">MonST3R</a>.
<h4 class="title is-3">
More results of video pose estimation can be found <a href="pose_est.html">here</a>.
</h4>
<!-- More results can be found <a href="pose_est.html">here</a>. -->
</div>
<div class="content">
<h2 style="text-align:left;"><strong>Application: Consistent video editting in 3D space</strong></h2>
<br>
<center>
<div class="video-container">
<video class='round' autoplay muted loop playsinline style='height:260px' src='resources/edit_videos/butterfly_edit_concat.mp4'></video>
<!-- <div class="hover-text">A hamster wearing virtual reality headsets is a DJ in a disco.</div> -->
</div>
</center>
<br>
<center>
<div class="video-container">
<video class='round' autoplay muted loop playsinline style='height:260px' src='resources/edit_videos/rollerblade_edit_concat.mp4'></video>
<!-- <div class="hover-text">A hamster wearing virtual reality headsets is a DJ in a disco.</div> -->
</div>
</center>
<br>
<center>
<div class="video-container">
<video class='round' autoplay muted loop playsinline style='height:260px' src='resources/edit_videos/scoccerball_edit_concat.mp4'></video>
<!-- <div class="hover-text">A hamster wearing virtual reality headsets is a DJ in a disco.</div> -->
</div>
</center>
</div>
<div class="content">
<!-- <h2 style="text-align:left;"><strong>Comparison with prior work</strong></h2> -->
<div>
<h4 class="title is-3"> Click <a href="compare_dense.html">here</a> for comparison with <a href="https://henry123-boy.github.io/SpaTracker/">SceneTracker</a> and <a href="https://henry123-boy.github.io/SpaTracker/">SpatialTracker</a> on 3D dense tracking.</h4>
<h4 class="title is-3"> Click <a href="compare_spatracker_tapvid3d.html">here</a> for comparison with <a href="https://henry123-boy.github.io/SpaTracker/">SpatialTracker</a> on the <a href="https://tapvid3d.github.io/">TAP-Vid3D</a> benchmark.</h4>
<!-- <h4 class="title is-3"> Click <a href="pose_est.html">here</a> for qualitative results of pose estimation.</h4> -->
<h4 class="title is-3"> Detailed quantitative results can be found in our <a href="files\paper.pdf">paper</a>.</h4>
<!-- <p> More quantitative results can be found in our paper </p> -->
</div>
</div>
<footer class="footer">
<div class="content">
<p>
<strong>Acknowledgements:</strong> We borrow this template from <a href="https://monst3r-project.github.io/">MonST3R</a> and <a href="https://snap-research.github.io/4Real/">4Real</a>.
The tracking visualization is inspired by <a href="https://co-tracker.github.io/">CoTracker</a>. The camera pose visualization tool is borrowed from <a href="https://monst3r-project.github.io/">MonST3R</a>.
We sincerely thank the authors for the open-source code.
</p>
</div>
</footer>
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