The nature of this type of research is that there are long term goals which are currently unachievable with no clear concept for how to approach them, so researchers need to start putting small pieces together and working out how to make it all work smoothly as a single concept. It looks like someone had a neural network for mouth movement. Someone had one for body movement, etc. Composing multiple systems in to one teaches us how we can approach more complex problems and how to better tie things together than just inserting the output of one in to the input of another.

Long term this type of work helps solve big problems even if the intermediate steps don’t produce exciting results.

As an example, early image generators were pretty uninteresting but today they are widely utilized and generally considered impressive. The thing that researchers in the field know that the public doesn’t is that there’s 100 boring steps before the exciting release, and some of the boring steps are very exciting on a technical level. Those intermediate achievements represent 99% of what machine learning research actually is and others in the field appreciate those works.