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<title>AIST++ Dataset - Download</title>
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<body onload="mainDivResize('download')" onresize="mainDivResize()">
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<div class="topnav" id="myTopnav">
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<a id="100" href="index.html" class="title">AIST++ Dataset</a>
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<a href="challenge2019.html" class="subitem">Past challenge: 2019</a>
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<a id="factsfigures" href="factsfigures.html" class="menuitem">Description</a>
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<a id="0" href="javascript:void(0);" style="font-size:15px;" class="icon" onclick="navbarResize()">&#9776;</a>
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<div style='max-width: 900px; margin: 0 auto;'>
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<br>
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Trouble downloading the dataset? <a href="mailto:aistplusplus-external@google.com">Let us know</a>.
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<h3>Download the videos/images/music</h3>
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If you're interested in downloading the full set of AIST Dance Videos (with music), please visit their
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<a href='https://aistdancedb.ongaaccel.jp/database_download/' target="_blank">website</a>.
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If you only need to download the subset of AIST Dance Videos that we annotated, we provide a
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Python script that downloads the videos (with music) from their website.
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<ol>
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<li>Download the file <a href="https://github.com/google/aistplusplus_api/blob/main/downloader.py" download="">downloader.py</a>
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(press <code>Ctrl + S</code>), or directly run:
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<pre>wget https://raw.githubusercontent.com/google/aistplusplus_api/main/downloader.py</pre>
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</li>
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<li>Run the following script:
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<pre>python downloader.py --download_folder=$DOWNLOAD_FOLDER --num_processes=5</pre>
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For help, run:
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<pre>python downloader.py -h</pre>
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</li>
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<li>Convert videos to images under <b>exact 60 FPS</b> instead of the raw fps.
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Our <a href='https://github.com/google/aistplusplus_api/'>Python API code</a>
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provides a <a href='https://github.com/google/aistplusplus_api/blob/main/aist_plusplus/utils.py#L22'>ffmpeg-based function</a>
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to do it but you are welcome to use alternative ways.
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</li>
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</ol>
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<h3 style="padding-top: 15px;">Download the annotations</h3>
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<div class="row">
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<div class="col-12" style="padding:0">
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<div class="col-3">
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<a href="https://storage.cloud.google.com/aist_plusplus_public/20121228/motions_only.zip">
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<button class="button">Motion Data Only (306MB)</button>
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</a>
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</div>
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<div class="col-3">
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<a href="https://storage.cloud.google.com/aist_plusplus_public/20121228/keypoints2d_only.zip">
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<button class="button">2D Keypoints Only (1.2GB)</button>
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</a>
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</div>
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<div class="col-3">
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<a href="https://storage.cloud.google.com/aist_plusplus_public/20121228/keypoints3d_only.zip">
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<button class="button">3D Keypoints Only (834MB)</button>
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</a>
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</div>
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<div class="col-3">
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<a href="https://storage.cloud.google.com/aist_plusplus_public/20121228/cameras_only.zip">
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<button class="button">Camera Data Only (19KB)</button>
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</a>
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</div>
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</div>
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</div>
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<div class="row">
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<div class="col-12" style="padding:0">
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<div class="col-12">
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<a href="https://storage.cloud.google.com/aist_plusplus_public/20121228/fullset.zip">
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<button class="button">Complete Dataset -- All Above + Dataset Splits (2.3GB)</button>
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</a>
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</div>
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</div>
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</div>
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<h2><a name="dataformats"></a> Data API</h2>
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<p>We provide the <a href="https://github.com/google/aistplusplus_api/">Python API code</a> for loading AIST++ annotations, as well as visualizing them.
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The API repository also contains reproducible code that we used to construct those annotations,
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including camera parameters estimation, 3D keypoints reconstruction and 3D human motion fitting.</p>
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<h2><a name="dataformats"></a> Data Formats</h2>
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<h3>Sequence Names</h3>
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<p>The annotations in the AIST++ are marked by <em>sequence name</em>. Each sequence name corresponds with a set of multi-view video names in the <a href="https://aistdancedb.ongaaccel.jp/">AIST Dance Video DB</a>.</p>
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<p>For example, <code>gBR_sBM_cAll_d04_mBR0_ch01</code> in our annotations corresponds with <code>gBR_sBM_{c01, ..., c09}_d04_mBR0_ch01</code> in the AIST database.</p>
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<h3>Camera Parameters</h3>
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<p>The camera intrinsic and extrinsic information is stored in two files.</p>
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<ul>
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<li>Individual environment settings, each with 9 cameras in the environment.
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(<code>setting_&lt;suffix&gt;.json</code>)</li>
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<li>Mapping from environment settings to sequence names.
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(<code>mapping.txt</code>)</li>
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</ul>
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<p>Each camera has the following attributes:</p>
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<ul>
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<li><code>name</code>: The camera name corresponding to the AIST database.</li>
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<li><code>size</code>: Canvas resolution. It is [1920, 1080] for all AIST 1080P video.</li>
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<li><code>matrix</code>: The 3x3 camera intrinsic matrix.*</li>
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<li><code>rotation</code>: The global rotation vector in Rodrigues format.*</li>
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<li><code>translation</code>: The global translation vector.*</li>
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<li><code>distortions</code>: Distortion coefficients.*</li>
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</ul>
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<p>*See <a href="https://docs.opencv.org/master/d9/d0c/group__calib3d.html#ga3207604e4b1a1758aa66acb6ed5aa65d">here</a> for detailed definition.</p>
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<h3>Human Motion Sequence</h3>
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<p>Each <a href="https://smpl.is.tue.mpg.de/">SMPL-format</a> human motion sequence is stored in a <code>.pkl</code> file with the following attributes:</p>
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<ul>
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<li><code>smpl_poses</code>: Sequences of SMPL pose parameters. Array shape is (N, 24, 3).</li>
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<li><code>smpl_scaling</code>: Human body scaling factor. A scalar value for each sequence.</li>
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<li><code>smpl_trans</code>: Motion 3D trajectory. Array shape is (N, 3).</li>
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</ul>
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<h3>3D&amp;2D Keypoints Annotation</h3>
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<p>We provide <a href="https://cocodataset.org/#home">COCO-format</a> keypoints annotation in both 2D and 3D. Each keypoints sequence is stored in a <code>.pkl</code> file with the following attributes:</p>
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<ul>
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<li><code>keypoints3d</code>: Sequences of 3D keypoints reconstructed frame-by-frame. Array shape is (N, 17, 3).</li>
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<li><code>keypoints3d_optim</code>: Sequences of 3D keypoints reconstructed with temporal smoothness and constrains.</li>
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<li><code>keypoints2d</code>: Multi-view frame-by-frame 2D keypoints detection results. Array shape is (9, N, 17, 3). The last dimension is (x, y, confidence).</li>
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</ul>
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<h3><a name="timestamps"></a> Timestamps</h3>
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<p>Our annotations are frame-by-frame under <strong>exact 60 FPS</strong>. Some videos in the <a href="https://aistdancedb.ongaaccel.jp/">AIST Dance Video DB</a> have slightly different FPS. In order to align frames across multi-view videos, we hard-coded <strong>60 FPS</strong> when converting videos into images.</p>
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<h3>Filter List</h3>
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<p>We manually checked our annotations and conducted numerical analysis. A filter list is provided that contains those sequences with poorly reconstructed 3D keypoints and human motion annotations (<code>ignore_list.txt</code>). We recommend excluding those annotations in your research or study.</p>
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</div>
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</div>
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</body>
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‎factsfigures.html‎

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<title>AIST++ Dataset - Description</title>
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The box that contain BibTeX code
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<a id="100" href="index.html" class="title">AIST++ Dataset</a>
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<a href="challenge2019.html" class="subitem">Past challenge: 2019</a>
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<div id="factsfigures_banner">
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<div style='max-width: 900px; margin: 0 auto;'>
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<h2>Overview of AIST++</h2>
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<div>
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<video style="width:100%" loop muted controls autoplay>
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<source src="images/dataset_example.mp4" type="video/mp4">
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</video>
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<p style="font-size: 12px"><i>The above video contains music. You can click the video to unmute it.</i></p>
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</div>
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</br>
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<p>The AIST++ Dance Motion Dataset is constructed from the <a href="https://aistdancedb.ongaaccel.jp/">AIST Dance Video DB</a>. With multi-view videos, an elaborate pipeline is designed to estimate the camera parameters, 3D human keypoints and 3D human dance motion sequences:</p>
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<ul>
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<li>It provides 3D human keypoint annotations and camera parameters for 10.1M images, covering 30 different subjects in 9 views. These attributes make it the <em>largest and richest existing dataset with 3D human keypoint annotations</em>.</li>
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<li>It also contains 1,408 sequences of 3D human dance motion, represented as joint rotations along with root trajectories. The dance motions are equally distributed among 10 dance genres with hundreds of choreographies. Motion durations vary from 7.4 sec. to 48.0 sec. All the dance motions have corresponding music.</li>
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</ul>
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<p>With those annotations, AIST++ is designed to support tasks including:</p>
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<ul>
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<li>Multi-view Human Keypoints Estimation.</li>
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<li>Human Motion Prediction/Generation.</li>
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<li>Cross-modal Analysis between Human Motion and Music.
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<br><br></li>
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</ul>
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<h2>Publications</h2>
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<p style="margin-block-end: 7px;">The following paper describes AIST++ dataset in depth: from the data processing to detailed statistics about the data. If you use the AIST++ dataset in your work, please cite this article.</p>
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<table style="border: 0;">
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<tr>
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<td width=17% style="border: 0;">
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<img src="images/paper_teaser.gif">
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</td>
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<td width=75% style="border: 0;">
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<i>Ruilong Li*, Shan Yang*, David Ross, Angjoo Kanazawa.</i><br>
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Learn to Dance with AIST++: Music Conditioned 3D Dance Generation.<br>
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In submission, 2020.<br>
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<a href="https://arxiv.org/abs/TODO" target="_blank">[PDF]</a> <a href="javascript:toggleBibtex('DanceGen')">[BibTeX]</a>
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</td>
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</tr>
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</table>
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<div id="bib_DanceGen" class="bibtex noshow">
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<pre>
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@inproceedings{TODO,
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author = {TODO},
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title = {Learn to Dance with AIST++: Music Conditioned 3D Dance Generation},
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booktitle = {TODO},
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year = {TODO}
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}
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</pre>
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</div>
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<p style="margin-top: 19px; margin-block-end: 7px;">Please also consider citing the original <a href="https://aistdancedb.ongaaccel.jp/">AIST Dance Video Database</a> if you find our dataset useful. (<a href="javascript:toggleBibtex('AIST')">[BibTex]</a>)</p>
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<div id="bib_AIST" class="bibtex noshow">
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<pre>
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@inproceedings{aist-dance-db,
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author = {Shuhei Tsuchida and Satoru Fukayama and Masahiro Hamasaki and Masataka Goto},
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title = {AIST Dance Video Database: Multi-genre, Multi-dancer, and Multi-camera Database for Dance Information Processing},
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booktitle = {Proceedings of the 20th International Society for Music Information Retrieval Conference, {ISMIR} 2019},
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address = {Delft, Netherlands},
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pages = {501--510},
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year = 2019,
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month = nov
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}
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</pre>
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</div>
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<h2>Dataset organization</h2>
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<p>The dataset is split into training/validation/testing sets in different ways serving for different purposes.</p>
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<ul>
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<li>For tasks such as human pose estimation and human motion prediction, we recommend using data splits described in Table 1. Here we split the trainval and testing sets based on different <em>subjects</em>, which also makes sure the human motions in the trainval and testing sets have no overlap. Note training and validation sets share the same group of subjects.</li>
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<li>For tasks dealing with motion and music such as music conditioned motion generation, we recommend using data splits described in Table 2. In the AIST database, the same music and the same choreography are shared by multiple human motion sequences. So we carefully split the dataset to make sure that the music and choreography in the training set does not overlap with that in the testing/validation set. Note validation and testing sets share the same group of music but different choreographies.</li>
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</ul>
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<p align='center'>Table 1: Data Splits based on Subjects.</p>
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<table>
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<thead>
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<tr>
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<th><div style="width:100px"></div></th>
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<th align="right">Train</th>
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<th align="right">Validation</th>
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<th align="right">Test</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>Images</td>
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<td align="right">6,420,059</td>
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<td align="right">508,234</td>
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<td align="right">3,179,722</td>
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</tr>
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<tr>
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<td>Sequences</td>
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<td align="right">868</td>
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<td align="right">70</td>
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<td align="right">470</td>
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</tr>
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<tr>
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<td>Subjects</td>
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<td align="right">20*</td>
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<td align="right">20*</td>
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<td align="right">10</td>
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</tr>
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</tbody>
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</table>
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<p align='center'>Table 2: Data Splits based on Music-Choreography.</p>
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<table>
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<thead>
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<tr>
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<th><div style="width:100px"></div></th>
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<th align="right">Train</th>
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<th align="right">Validation</th>
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<th align="right">Test</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>Seconds</td>
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<td align="right">13,963.6</td>
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<td align="right">187.6</td>
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<td align="right">187.6</td>
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</tr>
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<tr>
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<td>Sequences</td>
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<td align="right">980</td>
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<td align="right">20</td>
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<td align="right">20</td>
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</tr>
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<tr>
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<td>Choreographies</td>
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<td align="right">420</td>
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<td align="right">20</td>
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<td align="right">20</td>
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</tr>
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<tr>
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<td>Music</td>
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<td align="right">50</td>
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<td align="right">10*</td>
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<td align="right">10*</td>
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</tr>
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</tbody>
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</table>
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<p>*splits have shared data across this field.</p>
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<h2>Licenses</h2>
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<p>The annotations are licensed by Google LLC under <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a> license.</p>
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