MotionPersona: Characteristics-aware Locomotion Control

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:this https URL.

@article{shi2025_2506.00173,
  title={ MotionPersona: Characteristics-aware Locomotion Control },
  author={ Mingyi Shi and Wei Liu and Jidong Mei and Wangpok Tse and Rui Chen and Xuelin Chen and Taku Komura },
  journal={arXiv preprint arXiv:2506.00173},
  year={ 2025 }
}