chore: import upstream snapshot with attribution

tensorflow/lite/g3doc/examples/pose_estimation/overview.md

@@ -0,0 +1,249 @@
+# Pose estimation
+
+<img src="../images/pose.png" class="attempt-right" />
+
+Pose estimation is the task of using an ML model to estimate the pose of a
+person from an image or a video by estimating the spatial locations of key body
+joints (keypoints).
+
+## Get started
+
+If you are new to TensorFlow Lite and are working with Android or iOS, explore
+the following example applications that can help you get started.
+
+<a class="button button-primary" href="https://github.com/tensorflow/examples/tree/master/lite/examples/pose_estimation/android">
+Android example</a>
+<a class="button button-primary" href="https://github.com/tensorflow/examples/tree/master/lite/examples/pose_estimation/ios">
+iOS example</a>
+
+If you are familiar with the
+[TensorFlow Lite APIs](https://www.tensorflow.org/api_docs/python/tf/lite),
+download the starter MoveNet pose estimation model and supporting files.
+
+<a class="button button-primary" href="https://tfhub.dev/s?q=movenet"> Download
+starter model</a>
+
+If you want to try pose estimation on a web browser, check out the
+<a href="https://storage.googleapis.com/tfjs-models/demos/pose-detection/index.html?model=movenet">
+TensorFlow JS Demo</a>.
+
+## Model description
+
+### How it works
+
+Pose estimation refers to computer vision techniques that detect human figures
+in images and videos, so that one could determine, for example, where someone’s
+elbow shows up in an image. It is important to be aware of the fact that pose
+estimation merely estimates where key body joints are and does not recognize who
+is in an image or video.
+
+The pose estimation models takes a processed camera image as the input and
+outputs information about keypoints. The keypoints detected are indexed by a
+part ID, with a confidence score between 0.0 and 1.0. The confidence score
+indicates the probability that a keypoint exists in that position.
+
+We provides reference implementation of two TensorFlow Lite pose estimation
+models:
+
+*   MoveNet: the state-of-the-art pose estimation model available in two
+    flavors: Lighting and Thunder. See a comparison between these two in the
+    section below.
+*   PoseNet: the previous generation pose estimation model released in 2017.
+
+The various body joints detected by the pose estimation model are tabulated
+below:
+
+<table style="width: 30%;">
+  <thead>
+    <tr>
+      <th>Id</th>
+      <th>Part</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>0</td>
+      <td>nose</td>
+    </tr>
+    <tr>
+      <td>1</td>
+      <td>leftEye</td>
+    </tr>
+    <tr>
+      <td>2</td>
+      <td>rightEye</td>
+    </tr>
+    <tr>
+      <td>3</td>
+      <td>leftEar</td>
+    </tr>
+    <tr>
+      <td>4</td>
+      <td>rightEar</td>
+    </tr>
+    <tr>
+      <td>5</td>
+      <td>leftShoulder</td>
+    </tr>
+    <tr>
+      <td>6</td>
+      <td>rightShoulder</td>
+    </tr>
+    <tr>
+      <td>7</td>
+      <td>leftElbow</td>
+    </tr>
+    <tr>
+      <td>8</td>
+      <td>rightElbow</td>
+    </tr>
+    <tr>
+      <td>9</td>
+      <td>leftWrist</td>
+    </tr>
+    <tr>
+      <td>10</td>
+      <td>rightWrist</td>
+    </tr>
+    <tr>
+      <td>11</td>
+      <td>leftHip</td>
+    </tr>
+    <tr>
+      <td>12</td>
+      <td>rightHip</td>
+    </tr>
+    <tr>
+      <td>13</td>
+      <td>leftKnee</td>
+    </tr>
+    <tr>
+      <td>14</td>
+      <td>rightKnee</td>
+    </tr>
+    <tr>
+      <td>15</td>
+      <td>leftAnkle</td>
+    </tr>
+    <tr>
+      <td>16</td>
+      <td>rightAnkle</td>
+    </tr>
+  </tbody>
+</table>
+
+An example output is shown below:
+
+<img alt="Animation showing pose estimation" src="https://storage.googleapis.com/download.tensorflow.org/example_images/movenet_demo.gif"/>
+
+## Performance benchmarks
+
+MoveNet is available in two flavors:
+
+*   MoveNet.Lightning is smaller, faster but less accurate than the Thunder
+    version. It can run in realtime on modern smartphones.
+*   MoveNet.Thunder is the more accurate version but also larger and slower than
+    Lightning. It is useful for the use cases that require higher accuracy.
+
+MoveNet outperforms PoseNet on a variety of datasets, especially in images with
+fitness action images. Therefore, we recommend using MoveNet over PoseNet.
+
+Performance benchmark numbers are generated with the tool
+[described here](../../performance/measurement.md). Accuracy (mAP) numbers are
+measured on a subset of the [COCO dataset](https://cocodataset.org/#home) in
+which we filter and crop each image to contain only one person .
+
+<table>
+<thead>
+  <tr>
+    <th rowspan="2">Model</th>
+    <th rowspan="2">Size (MB)</th>
+    <th rowspan="2">mAP</th>
+    <th colspan="3">Latency (ms)</th>
+  </tr>
+  <tr>
+    <td>Pixel 5 - CPU 4 threads</td>
+    <td>Pixel 5 - GPU</td>
+    <td>Raspberry Pi 4 - CPU 4 threads</td>
+  </tr>
+</thead>
+<tbody>
+  <tr>
+    <td>
+      <a href="https://tfhub.dev/google/lite-model/movenet/singlepose/thunder/tflite/float16/4">MoveNet.Thunder (FP16 quantized)</a>
+    </td>
+    <td>12.6MB</td>
+    <td>72.0</td>
+    <td>155ms</td>
+    <td>45ms</td>
+    <td>594ms</td>
+  </tr>
+  <tr>
+    <td>
+      <a href="https://tfhub.dev/google/lite-model/movenet/singlepose/thunder/tflite/int8/4">MoveNet.Thunder (INT8 quantized)</a>
+    </td>
+    <td>7.1MB</td>
+    <td>68.9</td>
+    <td>100ms</td>
+    <td>52ms</td>
+    <td>251ms</td>
+  </tr>
+  <tr>
+    <td>
+      <a href="https://tfhub.dev/google/lite-model/movenet/singlepose/lightning/tflite/float16/4">MoveNet.Lightning (FP16 quantized)</a>
+    </td>
+    <td>4.8MB</td>
+    <td>63.0</td>
+    <td>60ms</td>
+    <td>25ms</td>
+    <td>186ms</td>
+  </tr>
+  <tr>
+    <td>
+      <a href="https://tfhub.dev/google/lite-model/movenet/singlepose/lightning/tflite/int8/4">MoveNet.Lightning (INT8 quantized)</a>
+    </td>
+    <td>2.9MB</td>
+    <td>57.4</td>
+    <td>52ms</td>
+    <td>28ms</td>
+    <td>95ms</td>
+  </tr>
+  <tr>
+    <td>
+      <a href="https://storage.googleapis.com/download.tensorflow.org/models/tflite/posenet_mobilenet_v1_100_257x257_multi_kpt_stripped.tflite">PoseNet(MobileNetV1 backbone, FP32)</a>
+    </td>
+    <td>13.3MB</td>
+    <td>45.6</td>
+    <td>80ms</td>
+    <td>40ms</td>
+    <td>338ms</td>
+  </tr>
+</tbody>
+</table>
+
+## Further reading and resources
+
+*   Check out this
+    [blog post](https://blog.tensorflow.org/2021/08/pose-estimation-and-classification-on-edge-devices-with-MoveNet-and-TensorFlow-Lite.html)
+    to learn more about pose estimation using MoveNet and TensorFlow Lite.
+*   Check out this
+    [blog post](https://blog.tensorflow.org/2021/05/next-generation-pose-detection-with-movenet-and-tensorflowjs.html)
+    to learn more about pose estimation on the web.
+*   Check out this [tutorial](https://www.tensorflow.org/hub/tutorials/movenet)
+    to learn about running MoveNet on Python using a model from TensorFlow Hub.
+*   Coral/EdgeTPU can make pose estimation run much faster on edge devices. See
+    [EdgeTPU-optimized models](https://coral.ai/models/pose-estimation/) for
+    more details.
+*   Read the PoseNet paper [here](https://arxiv.org/abs/1803.08225)
+
+Also, check out these use cases of pose estimation.
+
+<ul>
+  <li><a href="https://vimeo.com/128375543">‘PomPom Mirror’</a></li>
+  <li><a href="https://youtu.be/I5__9hq-yas">Amazing Art Installation Turns You Into A Bird | Chris Milk "The Treachery of Sanctuary"</a></li>
+  <li><a href="https://vimeo.com/34824490">Puppet Parade - Interactive Kinect Puppets</a></li>
+  <li><a href="https://vimeo.com/2892576">Messa di Voce (Performance), Excerpts</a></li>
+  <li><a href="https://www.instagram.com/p/BbkKLiegrTR/">Augmented reality</a></li>
+  <li><a href="https://www.instagram.com/p/Bg1EgOihgyh/">Interactive animation</a></li>
+  <li><a href="https://www.runnersneed.com/expert-advice/gear-guides/gait-analysis.html">Gait analysis</a></li>
+</ul>