Body and face motion play an integral role in communication. They convey crucial information on the participants. Advances in generative modeling and multi-modal learning have enabled motion generation from signals such as speech, conversational context and visual cues. However, generating expressive and coherent face and body dynamics remains challenging due to the complex interplay of verbal / non-verbal cues and individual personality traits. This survey reviews body and face motion generation, covering core concepts, representations techniques, generative approaches, datasets and evaluation metrics. We highlight future directions to enhance the realism, coherence and expressiveness of avatars in dyadic settings. To the best of our knowledge, this work is the first comprehensive review to cover both body and face motion. Detailed resources are listed on https://lownish23csz0010.github.io/mogen/.
@article{arxiv.2512.09005,
title = {A Survey of Body and Face Motion: Datasets, Performance Evaluation Metrics and Generative Techniques},
author = {Lownish Rai Sookha and Nikhil Pakhale and Mudasir Ganaie and Abhinav Dhall},
journal= {arXiv preprint arXiv:2512.09005},
year = {2025}
}