Transfer Learning Question: Extending the Functionality of a Multipose-Estimation Machine Learning Model?
I worked in a similar kind off object detection problem.so, my suggestion is Transfer Learning.

i)
The optimal way is to go with transfer learning.It allows us to build accurate models in a timesaving way.With transfer learning, instead of starting the learning process from scratch, you start from patterns that have been learned when solving a different problem. This way you leverage previous learnings and avoid starting from scratch.

A pre-trained model is a model that was trained on a large benchmark dataset to solve a problem similar to the one that we want to solve. Accordingly, due to the computational cost of training such models, it is common practice to import and use models from published literature (e.g. VGG, Inception, MobileNet).

Several pre-trained models used in transfer learning are based on large convolutional neural networks (CNN). 

When you’re repurposing a pre-trained model for your own needs, you start by removing the original classifier, then you add a new classifier that fits your purposes, and finally you have to fine-tune your model according to one of three strategies:

1.Train the model.
2.Train some layers and leave the frozen.
3.Freeze the convolutional base.


Approaches for Transfer learning process:

1)Select a pre-trained model.
2)Classify your problem according to the Size-Similarity Matrix. 
3)Fine-tune your model.


ii) Tensorflow offers a vision model named as PoseNet.

PoseNet is a vision model that can be used to estimate the pose of a person in an image or video by estimating where key body joints are.
From here you can freeze the model and fine tune it. orelse you can use other pretrained image model (e.g. mobilenet , resnet).

iii) The estimation directly from the raw images first request you to identify or detect the people , so it relays on another model.