Body Pose Estimation App ¶

Authors: Holoscan Team (NVIDIA)
Supported platforms: x86_64, aarch64
Last modified: March 24, 2025
Language: Python
Latest version: 1.0
Minimum Holoscan SDK version: 1.0.3
Tested Holoscan SDK versions: 1.0.3
Contribution metric: Level 2 - Trusted

Body pose estimation is a computer vision task that involves recognizing specific points on the human body in images or videos. A model is used to infer the locations of keypoints from the source video which is then rendered by the visualizer.

Model¶

This application uses YOLO11 pose model from Ultralytics for body pose estimation. The model is downloaded when building the application.

Data¶

This application downloads a pre-recorded video from Pexels when the application is built for use with this application. Please review the license terms from Pexels.

NOTE: The user is responsible for checking if the dataset license is fit for the intended purpose.

Input¶

This app currently supports three different input options:

v4l2 compatible input device (default, see V4L2 Support below)
pre-recorded video (see Video Replayer Support below)
DDS video stream (see DDS Support below)

Run Instructions¶

V4L2 Support¶

This application supports v4l2 compatible devices as input. To run this application with your v4l2 compatible device, please plug in your input device and run:

./dev_container build_and_run body_pose_estimation

By default, this application expects the input device to be mounted at /dev/video0. If this is not the case, please update applications/body_pose_estimation/body_pose_estimation.yaml and set it to use the corresponding input device before running the application. You can also override the default input device on the command line by running:

./dev_container build_and_run body_pose_estimation --run_args "--video_device /dev/video0"

Video Replayer Support¶

If you don't have a v4l2 compatible device plugged in, you may also run this application on a pre-recorded video. To launch the application using the Video Stream Replayer as the input source, run:

./dev_container build_and_run body_pose_estimation --run_args "--source replayer"

DDS Support¶

This application supports using a DDS video stream as the input as well as publishing the output video stream back to DDS. To enable DDS, the application must first be built with the DDS operators enabled. Only the subscriber or publisher operators need to be enabled for the sake of input or output video streams, respectively, but to enable both use the following:

./run build body_pose_estimation --with "dds_video_subscriber;dds_video_publisher"

Note that building these operators requires RTI Connext be installed. See the DDS Operator Documentation for more information on how to build the operators. If using a development container, see the additional instructions below.

To use a DDS video stream as the input to the application, use the -s=dds argument when running the application:

./run launch body_pose_estimation --extra_args -s=dds

To publish the output result to DDS, edit the body_pose_estimation.yaml configuration file so that the dds_publisher enable option is true:

dds_publisher:
  enable: true

Note that the default DDS video stream IDs use by the application are 0 for the input and 1 for the output. These can be changed using the stream_id settings in the dds_source and dds_publisher sections of the configuration file, respectively.

To produce the DDS input stream or to view the output stream generated by this application, the dds_video application can be used. For example, the following will use the dds_video application to capture video from the default V4L2 device and publish it to DDS so that it can be received as input by this application:

./run build dds_video
./run launch dds_video --extra_args "-p -i 0"

And the following will use the dds_video application to receive and render the output published by this application:

./run launch dds_video --extra_args "-s -i 1"

Using a Development Container with DDS Support¶

Installing RTI Connext into the development container is not currently supported, so enabling DDS support with this application requires RTI Connext be installed onto the host and then mounted into the container at runtime. To mount RTI Connext into the container, ensure that the NDDSHOME and CONNEXTDDS_ARCH environment variables are set (which can be done using the RTI setenv script) then use the following:

./dev_container launch --img holohub:bpe --docker_opts "-v $NDDSHOME:/opt/dds -e NDDSHOME=/opt/dds -e CONNEXTDDS_ARCH=$CONNEXTDDS_ARCH"

Known Issues¶

There is a known issue running this application on IGX w/ iGPU and on Jetson AGX (see #500). The workaround is to update the device to avoid picking up the libnvv4l2.so library.

cd /usr/lib/aarch64-linux-gnu/
ls -l libv4l2.so.0.0.999999
sudo rm libv4l2.so.0.0.999999
sudo ln -s libv4l2.so.0.0.0.0  libv4l2.so.0.0.999999

Body Pose Estimation App¶