Pressure2Motion：文本引导下基于地面压力的分层人体运动重建
Pressure2Motion: Hierarchical Human Motion Reconstruction from Ground Pressure with Text Guidance
把地面压力垫当作无摄像头、保护隐私的人体运动捕捉信号，用文本提示词消解压力到全身运动的不确定性；其分层扩散框架（先粗轨迹后细姿态）与双级压力特征提取器可迁移到其他欠定模态（如触觉、雷达）驱动的人体姿态估计。
原文依据 reconstructs human motion from a ground pressure sequence and text prompt
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论本文提出 Pressure2Motion，一种从地面压力序列与文本提示词重建人体运动的新型动作捕捉算法。在推理阶段，Pressure2Motion 仅需一块压力垫，无需专门的光照布置、相机或可穿戴设备，因而适用于保护隐私、低光照和低成本的动作捕捉场景。由于压力信号相对于全身运动存在不确定性，该任务严重病态。为解决这一问题，我们提出 Pressure2Motion，一种生成模型，以压力特征为输入，并利用文本提示词作为高层引导约束来消解歧义。具体而言，该模型采用双级特征提取器以准确解读压力数据，随后使用分层扩散模型辨别粗尺度运动轨迹与细微姿态调整。从压力序列获得的物理线索与从描述性文本中得到的语义引导共同用于精确引导运动估计。据我们所知，Pressure2Motion 是同时利用压力数据与语言先验进行运动重建的开创性工作，所建立的 MPL 基准是该新型动作捕捉任务的首个基准。实验表明，本方法能够生成高保真、物理合理的运动，为该任务确立了新的最先进水平。代码与基准将在论文发表后公开。
英文原文摘要 1544 字符
We present Pressure2Motion, a novel motion capture algorithm that reconstructs human motion from a ground pressure sequence and text prompt. At inference time, Pressure2Motion requires only a pressure mat, eliminating 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 indeterminacy of pressure signals with respect 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 to resolve ambiguities. Specifically, our model adopts a dual-level feature extractor to accurately interpret 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 estimation with precision. To the best of our knowledge, Pressure2Motion is a pioneering work in leveraging both pressure data and linguistic priors for motion reconstruction, and the established MPL benchmark is the first benchmark for this novel motion capture task. Experiments show that 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.