Pressure2Motion: Hierarchical Motion Synthesis from Ground Pressure with Text Guidance

2025-11-07

AI Summary

This work addresses the challenge of human motion generation under privacy-sensitive, low-light, and cost-constrained settings—without cameras, specialized lighting, or wearable sensors. The proposed method synthesizes physically plausible full-body poses solely from ground pressure sequences and natural language prompts. It innovatively integrates pressure signal dynamics with linguistic priors via a dual-level feature extractor that captures spatiotemporal pressure patterns, coupled with a hierarchical diffusion architecture: first generating coarse global trajectories, then refining them into kinematically and physically consistent pose sequences. To support systematic evaluation, the authors introduce MPL (Motion from Pressure and Language), the first benchmark dedicated to this task. Extensive experiments demonstrate state-of-the-art performance across motion fidelity, physical plausibility, and text-pose alignment metrics.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Intelligent Robots: Motion and Path Planning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

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.

Problem

• Generates human motion from ground pressure data
• Uses text prompts as semantic guidance constraints
• Enables privacy-preserving low-cost motion capture

Innovation

• Generative model using pressure features and text prompts
• Dual-level feature extractor interpreting pressure data accurately
• Hierarchical diffusion model for movement trajectories and postures