Hwang, JaepyungPark, GangraeKwon, TaesooIshii, ShinHauser, Helwig and Alliez, Pierre2022-10-112022-10-1120221467-8659https://doi.org/10.1111/cgf.14499https://diglib.eg.org:443/handle/10.1111/cgf14499In this study, we focus on developing a motion synthesis framework that generates a natural transition motion between two different behaviours to interact with a moving object. Specifically, the proposed framework generates the transition motion, bridging from a locomotive behaviour to an object interaction behaviour. And, the transition motion should adapt to the spatio‐temporal variation of the target object in an online manner, so as to naturally connect the behaviours. To solve this issue, we propose a framework that combines a regression model and a transition motion planner. The neural network‐based regression model estimates the reference transition strategy to guide the reference pattern of the transitioning, adapted to the varying situation. The transition motion planner reconstructs the transition motion based on the reference pattern while considering dynamic constraints that avoid the footskate and interaction constraints. The proposed framework is validated to synthesize various transition motions while adapting to the spatio‐temporal variation of the object by using object grasping motion, and athletic motions in soccer.animation systemsanimationanimation w/constraints10.1111/cgf.1449937-50