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We address the problem of generating 3D human motions in dyadic activities.In contrast to the concurrent works, which mainly focus on generating themotion of a single actor from the textual description, we generate the motionof one of the actors from the motion of the other participating actor in theaction. This is a particularly challenging, under-explored problem, thatrequires learning intricate relationships between the motion of two actorsparticipating in an action and also identifying the action from the motion ofone actor. To address these, we propose partner conditioned motion operator(PaCMO), a neural operator-based generative model which learns the distributionof human motion conditioned by the partner's motion in function spaces throughadversarial training. Our model can handle long unlabeled action sequences atarbitrary time resolution. We also introduce the "Functional Frechet InceptionDistance" ($F^2ID$) metric for capturing similarity between real and generateddata for function spaces. We test PaCMO on NTU RGB+D and DuetDance datasets andour model produces realistic results evidenced by the $F^2ID$ score and theconducted user study.

PaCMO: Partner Dependent Human Motion Generation in Dyadic Human Activity using Neural Operators