2D or not 2D: How Does the Dimensionality of Gesture Representation Affect 3D Co-Speech Gesture Generation?

However, most of the recent literature considers 3D motion data [4-6, 14, 42, 50, 53-55, 58], primarily because such data representation Co-speech gestures are fundamental for communication. The advent contains the depth dimension and is more easily transferable to of recent deep learning techniques has facilitated the creation downstream applications such as 3D virtual agents or social robots of lifelike, synchronous co-speech gestures for Embodied Conversational [43, 56]. But, it is not easy to collect high-quality 3D motion data, Agents. "In-the-wild" datasets, aggregating video content as one needs a motion capture setup in a controlled environment, from platforms like YouTube via human pose detection technologies, hence limiting the size and diversity of such datasets. To access provide a feasible solution by offering 2D skeletal sequences aligned 3D motion data and still gather large-scale datasets of diverse and with speech. Concurrent developments in lifting models enable the spontaneous gestures, multiple works leverage an estimation of the conversion of these 2D sequences into 3D gesture databases. However, 3D gestures inferred from 2D poses extracted from "in-the-wild" it is important to note that the 3D poses estimated from the videos [39, 50, 55]. Nevertheless, to convert extracted 2D keypoints 2D extracted poses are, in essence, approximations of the groundtruth, to 3D, one needs a third-party 2D-to-3D lifter, which may be prone which remains in the 2D domain. This distinction raises to inaccuracies, notably because of the ambiguous nature of 3D questions about the impact of gesture representation dimensionality pose estimation from 2D keypoints [44].