  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. Given the motion of
one person, 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 persons 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.
