This is an entertaining question. We'll use some neural networks from the neural network repository to attempt to solve it.
We'll use Ademxapp model, so here's a function to evaluate the net and give us back masks for each type of object it detects.
net = NetModel["Ademxapp Model A1 Trained on ADE20K Data"];
netevaluate[img_, device_: "CPU"] :=
Block[{resized, encData, dec, mean, var, prob},
resized = ImageResize[img, {504}];
encData = Normal@NetExtract[net, "Input"];
dec = NetExtract[net, "Output"];
{mean, var} = Lookup[encData, {"MeanImage", "VarianceImage"}];
prob = NetReplacePart[
net, {"Input" ->
NetEncoder[{"Image", ImageDimensions@resized,
"MeanImage" -> mean, "VarianceImage" -> var}],
"Output" -> Automatic}][resized, TargetDevice -> device];
prob = ArrayResample[prob, Append[Reverse@ImageDimensions@img, 150]];
dec[prob]]
Now we'll write a function to only get data about people in the image. Now, from the documentation in the repository, I know that the label for the "person" mask is 13, and that's the only mask that we care about.
getPeople[i_] := Map[ReplaceAll[{13 -> 1, _ -> 0}], netevaluate[i], {2}] // Image

Now we can simply get the largest item in that mask and remove it. We could try Inpaint
but it didn't work very well on this image.
removePresenter[i_] := ImageAdd[i, Dilation[SelectComponents[getPeople[i], "Count", -1], 3]]

Try playing with the argument to Dilation
if it doesn't take enough of the presenter. I would also consider changing the neural network for others in the "Semantic Segmentation" section if this one isn't accurate enough for you.