Hello
There is a 3d human pose estimator (HPE) that supported by deepstream
and TAO pretrained models for this 3d HPE
Question 1:
How to use bodypose3dnet for training/retraining using custom datasets and what exactly should be format, configuration and procedure in general ?
For instance, what are represent tensors 1, 2, 3 and 4
k_inv
t_form_inv
scale_normalized_mean_limb_lengths
mean_limb_lengths
For instance, for bodyposenet (2d) there’s much more documentation on how to work with it, e.g. prepare training training, correct format, etc.
https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/**bodyposenet**
Body Pose Estimation — TAO Toolkit 3.22.05 documentation
https://developer.nvidia.com/blog/training-optimizing-2d-pose-estimation-model-with-tao-toolkit-part-1/
https://developer.nvidia.com/blog/training-optimizing-2d-pose-estimation-model-with-tao-toolkit-part-2/
Question 2:
What are the minimum hardware requirements for inference using deepstream_reference_apps/deepstream-bodypose-3d at master · NVIDIA-AI-IOT/deepstream_reference_apps · GitHub and 2 available models on NGC
bodypose3dnet_performance.etlt
bodypose3dnet_accuracy.etlt
I assume that it should work on Jetson family devices since this repository is located in
GitHub - NVIDIA-AI-IOT/deepstream_reference_apps: Samples for TensorRT/Deepstream for Tesla & Jetson
and it is mentioned in description of the repository “Samples for TensorRT/Deepstream for Tesla & Jetson”,
but I am not sure which ones are supported. I am mostly interested in Jetson Xavier NX and Orin NX or possibly even lower end device.
Thank you
Please provide the following information when requesting support.
• Hardware V100 & Xavier NX
• Network Type (bodypose3dnet BodyPose3DNet | NVIDIA NGC)