MotionPersona: Characteristics-aware Locomotion Control
Mingyi Shi, Wei Liu, Jidong Mei, Wangpok Tse, Rui Chen, Xuelin Chen, Taku Komura

TL;DR
MotionPersona is a real-time character controller that generates diverse, characteristic-aware locomotion by conditioning on traits, prompts, and control signals, outperforming existing methods in quality and diversity.
Contribution
It introduces the first characteristics-aware, real-time locomotion generator conditioned on traits and prompts, with a new dataset and a few-shot characterization technique.
Findings
Outperforms existing methods in motion quality and diversity.
Capable of generating motion reflecting user-specified traits.
Supports real-time response to dynamic control inputs.
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…
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Taxonomy
TopicsHuman Motion and Animation · Teleoperation and Haptic Systems · Hand Gesture Recognition Systems
