A language‐directed virtual human motion generation approach based on musculoskeletal models - Sun, Libo; Wang, Yongxiang; Qin, Wenhu (Computer animation and virtual worlds, 2024)
The development of the systems capable of synthesizing natural and life‐like motions for virtual characters has long been a central focus in computer animation. It needs to generate high‐quality motions for characters and provide users with a convenient and flexible interface for guiding character motions. In this work, we propose a language‐directed virtual human motion generation approach based on musculoskeletal models to achieve interactive and higher‐fidelity virtual human motion, which lays the foundation for the development of language‐directed controllers in physics‐based character animation. First, we construct a simplified model of musculoskeletal dynamics for the virtual character. Subsequently, we propose a hierarchical control framework consisting of a trajectory tracking layer and a muscle control layer, obtaining the optimal control policy for imitating the reference motions through the training. We design a multi‐policy aggregation controller based on large language models, which selects the motion policy with the highest similarity to user text commands from the action‐caption data pool, facilitating natural language‐based control of virtual character motions. Experimental results demonstrate that the proposed approach not only generates high‐quality motions highly resembling reference motions but also enables users to effectively guide virtual characters to perform various motions via natural language instructions. We propose a language‐directed virtual human motion generation approach based on musculoskeletal models to achieve interactive and higher‐fidelity virtual human motion. It takes reference motion data, caption and text prompts as inputs, realizing the natural language motion controller through three components: constructing an action‐caption data pool, learning the control policies for imitating the motion, and semantic matching selection.