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arXiv · 2506.00173

MotionPersona: Characteristics-aware Locomotion Control

Mingyi Shi·Wei Liu·Jidong Mei·Wangpok Tse·Rui Chen·Xuelin Chen·Taku Komura
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Abstract

We present MotionPersona, a novel real-time character controller that allows users to characterize a character by specifying attributes such as physical traits, mental states, and demographics, and projects these properties into the generated motions for animating the character. In contrast to existing deep learning-based controllers, which typically produce homogeneous animations tailored to a single, predefined character, MotionPersona accounts for the impact of various traits on human motion as observed in the real world. To achieve this, we develop a block autoregressive motion diffusion model conditioned on SMPLX parameters, textual prompts, and user-defined locomotion control signals. We also curate a comprehensive dataset featuring a wide range of locomotion types and actor traits to enable the training of this characteristic-aware controller. Unlike prior work, MotionPersona is the first method capable of generating motion that faithfully reflects user-specified characteristics (e.g., an elderly person's shuffling gait) while responding in real time to dynamic control inputs. Additionally, we introduce a few-shot characterization technique as a complementary conditioning mechanism, enabling customization via short motion clips when language prompts fall short. Through extensive experiments, we demonstrate that MotionPersona outperforms existing methods in characteristics-aware locomotion control, achieving superior motion quality and diversity. Results, code, and demo can be found at: https://motionpersona25.github.io/.

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Mingyi Shi, Wei Liu, Jidong Mei, Wangpok Tse, Rui Chen, Xuelin Chen, Taku Komura. 2025-05-30. MotionPersona: Characteristics-aware Locomotion Control. https://arxiv.org/abs/2506.00173

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