InsActor：基於指令驅動的基於物理的角色

InsActor: Instruction-driven Physics-based Characters

December 28, 2023

作者: Jiawei Ren, Mingyuan Zhang, Cunjun Yu, Xiao Ma, Liang Pan, Ziwei Liu

cs.AI

摘要

生成基於物理的角色動畫並實現直觀控制一直是一項令人嚮往且具有眾多應用的任務。然而，生成反映高層人類指令的物理模擬動畫仍然是一個困難問題，這是由於物理環境的複雜性和人類語言的豐富性所導致的。本文提出了InsActor，一個基於原則的生成框架，利用最近在擴散式人體運動模型方面的進展，來產生基於物理的角色的指令驅動動畫。我們的框架賦予InsActor捕捉高層人類指令與角色動作之間複雜關係的能力，通過使用擴散策略進行靈活條件化的運動規劃。為了克服計劃動作中的無效狀態和不可行的狀態轉換，InsActor發現低層技能，並將計劃映射到緊湊的潛在技能序列空間中。大量實驗表明，InsActor在各種任務上取得了最先進的成果，包括基於指令的運動生成和基於指令的航向路徑生成。值得注意的是，InsActor能夠使用高層人類指令生成物理模擬動畫，使其成為一個寶貴的工具，特別是在執行具有豐富指令集的長視程任務時。

English

Generating animation of physics-based characters with intuitive control has long been a desirable task with numerous applications. However, generating physically simulated animations that reflect high-level human instructions remains a difficult problem due to the complexity of physical environments and the richness of human language. In this paper, we present InsActor, a principled generative framework that leverages recent advancements in diffusion-based human motion models to produce instruction-driven animations of physics-based characters. Our framework empowers InsActor to capture complex relationships between high-level human instructions and character motions by employing diffusion policies for flexibly conditioned motion planning. To overcome invalid states and infeasible state transitions in planned motions, InsActor discovers low-level skills and maps plans to latent skill sequences in a compact latent space. Extensive experiments demonstrate that InsActor achieves state-of-the-art results on various tasks, including instruction-driven motion generation and instruction-driven waypoint heading. Notably, the ability of InsActor to generate physically simulated animations using high-level human instructions makes it a valuable tool, particularly in executing long-horizon tasks with a rich set of instructions.