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‎docs/assets/12_view_assembly.mp4

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‎docs/assets/Assembly101.pdf

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<head>
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<title>Assembly101: A Large-Scale Multi-View Video Dataset for Understanding Procedural Activities</title>
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<meta property="og:description" content="Assembly101: A Large-Scale Multi-View Video Dataset for Understanding Procedural Activities"/>
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<h1>Assembly101: A Large-Scale Multi-View Video Dataset for Understanding Procedural Activities</h1>
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</div>
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<div id="authors">
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<div class="col-4 text-center"><a href="https://scholar.google.com/citations?user=-juoweoAAAAJ">Fadime Sener<sup>1</sup></a></div>
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<div class="col-4 text-center"><a href="">Dibyadip Chatterjee<sup>2</sup></a></div>
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<div class="col-4 text-center"><a href="">Daniel Shelepov<sup>1</sup></a></div>
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<div class="col-4 text-center"><a href="">Kun He<sup>1</sup></a></div>
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<div class="col-4 text-center"><a href="">Dipika Singhania<sup>2</sup></a></div>
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<div class="col-4 text-center"><a href="">Robert Wang<sup>1</sup></a></div>
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<div class="col-4 text-center"><a href="">Angela Yao<sup>2</sup></a></div>
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<div class="col-1 text-center"><sup>1</sup>Reality Labs at Meta</div>
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<div class="col-1 text-center"><sup>2</sup>National University of Singapore</div>
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<a class="paper-btn" href="assets/Assembly101.pdf">
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<span class="material-icons"> description </span>
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Paper
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<span class="material-icons"> videocam </span>
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Dataset
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<a class="paper-btn" href="https://github.com/assembly101/assembly101">
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Code
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Assembly101 is a large-scale video dataset for action recognition and markerless motion capture of hand-object interactions, captured in the above cage setting.
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The recordings, which are multi-view captures, feature participants assembling 101 children's toys.
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<h2>News</h2>
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<div><span class="material-icons"> description </span> [March 28th 2022] Paper released on <a href="https://arxiv.org/pdf/2203.14712.pdf">arXiv</a>.</div>
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<div><span class="material-icons"> integration_instructions </span> [March 28th 2022] Dataset released on <a href="https://drive.google.com/drive/folders/1nh8PHwEw04zxkkkKlfm4fsR3IPEDvLKj?usp=sharing">Google Drive</a>.</div>
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<section id="abstract"/>
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<h2>Abstract</h2>
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<p>
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Assembly101 is a new procedural activity dataset featuring 4321 videos of people assembling and disassembling 101 "take-apart" toy vehicles. Participants work without fixed instructions, and the sequences feature rich and natural variations in action ordering, mistakes, and corrections. Assembly101 is the first multi-view action dataset, with simultaneous static (8) and egocentric (4) recordings. Sequences are annotated with more than 100K coarse and 1M fine-grained action segments, and 18M 3D hand poses. We benchmark on three action understanding tasks: recognition, anticipation and temporal segmentation. Additionally, we propose a novel task of detecting mistakes. The unique recording format and rich set of annotations allow us to investigate generalization to new toys, cross-view transfer, long-tailed distributions, and pose vs. appearance. We envision that Assembly101 will serve as a new challenge to investigate various activity understanding problems.
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<h2>Paper</h2>
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<div style="box-sizing: border-box; padding: 16px; margin: auto;">
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<a href="assets/paper.pdf"><img class="screenshot" src="assets/assembly101_rig.png"></a>
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</div>
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<div style="width: 60%">
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<p><b>Assembly101: A Large-Scale Multi-View Video Dataset for Understanding Procedural Activities</b></p>
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<p>Fadime Sener, Dibyadip Chatterjee, Daniel Shelepov, Kun He, Dipika Singhania, Robert Wang, Angela Yao</p>
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<div><span class="material-icons"> description </span><a href="assets/Assembly101.pdf"> Paper preprint </a></div>
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<div><span class="material-icons"> description </span><a href="assets/Assembly101_supp.pdf"> Supplementary </a></div>
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<div><span class="material-icons"> description </span><a href="https://arxiv.org/pdf/2203.14712.pdf"> arXiv version</a></div>
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<div><span class="material-icons"> integration_instructions </span><a href="https://github.com/assembly101/assembly101"> Code</a></div>
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<!-- <div><span class="material-icons"> videocam </span><a href="https://youtu.be/asdf"> Video</a></div>-->
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<p>Please send feedback and questions to <tt>3dassembly101&lt;at&gt;gmail.com</tt></p>
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</section>
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<section id="bibtex">
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<h2>Citation</h2>
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<pre><code>@article{sener2022assembly101,
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title = {Assembly101: A Large-Scale Multi-View Video Dataset for Understanding Procedural Activities},
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author = {F. Sener and D. Chatterjee and D. Shelepov and K. He and D. Singhania and R. Wang and A. Yao},
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journal = {CVPR 2022},
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}
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</code></pre>
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