ML Community Day is November 9! Join us for updates from TensorFlow, JAX, and more Learn more

Pose estimation

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.

Android example iOS example

If you are familiar with the TensorFlow Lite APIs, download the starter MoveNet pose estimation model and supporting files.

Download starter model

If you want to try pose estimation on a web browser, check out the TensorFlow JS Demo.

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:

Id Part
0 nose
1 leftEye
2 rightEye
3 leftEar
4 rightEar
5 leftShoulder
6 rightShoulder
7 leftElbow
8 rightElbow
9 leftWrist
10 rightWrist
11 leftHip
12 rightHip
13 leftKnee
14 rightKnee
15 leftAnkle
16 rightAnkle

An example output is shown below:

Animation showing pose estimation

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. Accuracy (mAP) numbers are measured on a subset of the COCO dataset in which we filter and crop each image to contain only one person .

Model Size (MB) mAP Latency (ms)
Pixel 5 - CPU 4 threads Pixel 5 - GPU Raspberry Pi 4 - CPU 4 threads
MoveNet.Thunder (FP16 quantized) 12.6MB 72.0 155ms 45ms 594ms
MoveNet.Thunder (INT8 quantized) 7.1MB 68.9 100ms 52ms 251ms
MoveNet.Lightning (FP16 quantized) 4.8MB 63.0 60ms 25ms 186ms
MoveNet.Lightning (INT8 quantized) 2.9MB 57.4 52ms 28ms 95ms
PoseNet(MobileNetV1 backbone, FP32) 13.3MB 45.6 80ms 40ms 338ms

Further reading and resources

  • Check out this blog post to learn more about pose estimation using MoveNet and TensorFlow Lite.
  • Check out this blog post to learn more about pose estimation on the web.
  • Check out this tutorial to learn about running MoveNet on Python using a model from TensorFlow Hub.
  • Coral/EdgeTPU can make pose estimation run much faster on IoT devices. See EdgeTPU-optimized models for more details.
  • Read the PoseNet paper here

Also, check out these use cases of pose estimation.