Pressure2Motion: Hierarchical Motion Synthesis from Ground Pressure with Text Guidance

We present Pressure2Motion, a novel motion capture algorithm that synthesizes human motion from a ground pressure sequence and text prompt. It eliminates the need for specialized lighting setups, cameras, or wearable devices, making it suitable for privacy-preserving, low-light, and low-cost motion capture scenarios. Such a task is severely ill-posed due to the indeterminate nature of the pressure signals to full-body motion. To address this issue, we introduce Pressure2Motion, a generative model that leverages pressure features as input and utilizes a text prompt as a high-level guiding constraint. Specifically, our model utilizes a dual-level feature extractor that accurately interprets pressure data, followed by a hierarchical diffusion model that discerns broad-scale movement trajectories and subtle posture adjustments. Both the physical cues gained from the pressure sequence and the semantic guidance derived from descriptive texts are leveraged to guide the motion generation with precision. To the best of our knowledge, Pressure2Motion is a pioneering work in leveraging both pressure data and linguistic priors for motion generation, and the established MPL benchmark is the first benchmark for this task. Experiments show our method generates high-fidelity, physically plausible motions, establishing a new state-of-the-art for this task. The codes and benchmarks will be publicly released upon publication.