Pose augmentation

Windle, Jonathan; Taylor, Sarah L.; Greenwood, David A.; Matthews, Iain

doi:10.1145/3514197.3549677

Cited by 2 publications

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recent work has also demonstrated the possibility of learning to embed different gesture styles, which then can be used for zero‐shot adaptation to the style of an unseen target speaker with no training data of the target speaker [FGPO22, GFH*23]. Techniques for augmenting gesture data so as to increase the amount of motion data for training have also been studied, especially mirroring [WTGM22b, WTGM22a].…”

Section: Data‐driven Approachesmentioning

confidence: 99%

A Comprehensive Review of Data‐Driven Co‐Speech Gesture Generation

Nyatsanga

Kucherenko²,

Ahuja³

et al. 2023

Computer Graphics Forum

View full text Add to dashboard Cite

Gestures that accompany speech are an essential part of natural and efficient embodied human communication. The automatic generation of such co‐speech gestures is a long‐standing problem in computer animation and is considered an enabling technology for creating believable characters in film, games, and virtual social spaces, as well as for interaction with social robots. The problem is made challenging by the idiosyncratic and non‐periodic nature of human co‐speech gesture motion, and by the great diversity of communicative functions that gestures encompass. The field of gesture generation has seen surging interest in the last few years, owing to the emergence of more and larger datasets of human gesture motion, combined with strides in deep‐learning‐based generative models that benefit from the growing availability of data. This review article summarizes co‐speech gesture generation research, with a particular focus on deep generative models. First, we articulate the theory describing human gesticulation and how it complements speech. Next, we briefly discuss rule‐based and classical statistical gesture synthesis, before delving into deep learning approaches. We employ the choice of input modalities as an organizing principle, examining systems that generate gestures from audio, text and non‐linguistic input. Concurrent with the exposition of deep learning approaches, we chronicle the evolution of the related training data sets in terms of size, diversity, motion quality, and collection method (e.g., optical motion capture or pose estimation from video). Finally, we identify key research challenges in gesture generation, including data availability and quality; producing human‐like motion; grounding the gesture in the co‐occurring speech in interaction with other speakers, and in the environment; performing gesture evaluation; and integration of gesture synthesis into applications. We highlight recent approaches to tackling the various key challenges, as well as the limitations of these approaches, and point toward areas of future development.

show abstract

Section: Data‐driven Approachesmentioning

confidence: 99%