  Co-speech gestures play a crucial role in the interactions between humans and
embodied conversational agents (ECA). Recent deep learning methods enable the
generation of realistic, natural co-speech gestures synchronized with speech,
but such approaches require large amounts of training data. "In-the-wild"
datasets, which compile videos from sources such as YouTube through human pose
detection models, offer a solution by providing 2D skeleton sequences that are
paired with speech. Concurrently, innovative lifting models have emerged,
capable of transforming these 2D pose sequences into their 3D counterparts,
leading to large and diverse datasets of 3D gestures. However, the derived 3D
pose estimation is essentially a pseudo-ground truth, with the actual ground
truth being the 2D motion data. This distinction raises questions about the
impact of gesture representation dimensionality on the quality of generated
motions, a topic that, to our knowledge, remains largely unexplored. In this
work, we evaluate the impact of the dimensionality of the training data, 2D or
3D joint coordinates, on the performance of a multimodal speech-to-gesture deep
generative model. We use a lifting model to convert 2D-generated sequences of
body pose to 3D. Then, we compare the sequence of gestures generated directly
in 3D to the gestures generated in 2D and lifted to 3D as post-processing.
