ReMoS: 3D Motion-Conditioned Reaction Synthesis for Two-Person Interactions
Abstract
Current approaches for 3D human motion synthesis generate high-quality animations of digital humans performing a wide variety of actions and gestures. However, a notable technological gap exists in addressing the complex dynamics of multi-human interactions within this paradigm. In this work, we present ReMoS, a denoising diffusion- based model that synthesizes full-body reactive motion of a person in a two-person interaction scenario. Assuming the motion of one person is given, we employ a combined spatio-temporal cross-attention mechanism to synthesize the reactive body and hand motion of the second person, thereby completing the interactions between the two. We demonstrate ReMoS across challenging two-person scenarios such as pair-dancing, Ninjutsu, kickboxing, and acrobatics, where one person’s movements have complex and diverse influences on the other. We also contribute the ReMoCap dataset for two-person interactions containing full-body and finger motions. We evaluate ReMoS through multiple quantitative metrics, qualitative visualizations, and a user study, and also indicate usability in interactive motion editing applications
ReMoCap Dataset
We propose the ReMoCap dataset for two-person interactions consisting of fullbody and hand motions. The dataset captures interactive, challenging two-person motions in two scenarios: the fast-paced swing style Lindy Hop dancing and the martial art technique of Ninjutsu.
Video
Quantitative Evaluation
Downloads
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arXiv
Citation
@InProceedings{ghosh2023remos,
title={ReMoS: 3D Motion-Conditioned Reaction Synthesis for Two-Person Interactions},
author={Ghosh, Anindita and Dabral, Rishabh and Golyanik, Vladislav and Theobalt, Christian and Slusallek, Philipp},
booktitle={arXiv},
year={2023}
}
Contact
For questions, clarifications, please get in touch with:Anindita Ghosh
anghosh@mpi-inf.mpg.de