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Generating animation of physics-based characters with intuitive control haslong been a desirable task with numerous applications. However, generatingphysically simulated animations that reflect high-level human instructionsremains a difficult problem due to the complexity of physical environments andthe richness of human language. In this paper, we present InsActor, aprincipled generative framework that leverages recent advancements indiffusion-based human motion models to produce instruction-driven animations ofphysics-based characters. Our framework empowers InsActor to capture complexrelationships between high-level human instructions and character motions byemploying diffusion policies for flexibly conditioned motion planning. Toovercome invalid states and infeasible state transitions in planned motions,InsActor discovers low-level skills and maps plans to latent skill sequences ina compact latent space. Extensive experiments demonstrate that InsActorachieves state-of-the-art results on various tasks, includinginstruction-driven motion generation and instruction-driven waypoint heading.Notably, the ability of InsActor to generate physically simulated animationsusing high-level human instructions makes it a valuable tool, particularly inexecuting long-horizon tasks with a rich set of instructions.

InsActor: Instruction-driven Physics-based Characters