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This paper proposes the Transition Motion Tensor, a data-driven frameworkthat creates novel and physically accurate transitions outside of the motiondataset. It enables simulated characters to adopt new motion skills efficientlyand robustly without modifying existing ones. Given several physicallysimulated controllers specializing in different motions, the tensor serves as atemporal guideline to transition between them. Through querying the tensor fortransitions that best fit user-defined preferences, we can create a unifiedcontroller capable of producing novel transitions and solving complex tasksthat may require multiple motions to work coherently. We apply our framework onboth quadrupeds and bipeds, perform quantitative and qualitative evaluations ontransition quality, and demonstrate its capability of tackling complex motionplanning problems while following user control directives.

Transition Motion Tensor: A Data-Driven Approach for Versatile and Controllable Agents in Physically Simulated Environments