  Recent work have addressed the generation of human poses represented by 2D/3D
coordinates of human joints for sign language. We use the state of the art in
Deep Learning for motion transfer and evaluate them on How2Sign, an American
Sign Language dataset, to generate videos of signers performing sign language
given a 2D pose skeleton. We evaluate the generated videos quantitatively and
qualitatively showing that the current models are not enough to generated
adequate videos for Sign Language due to lack of detail in hands.
