用生成模型提升深度图转压力分布的物理合理性，支持实时临床监测。
Improving the Plausibility of Pressure Distributions Synthesized from Depth Image through Generative Modeling
- 基于条件生成模型，引入有信息的潜在空间和权重优化损失
- 在多个数据集上压力分布物理一致性提升18.7%，推理速度更快
- 适合医疗健康领域做非侵入式、实时患者压力监测
监测医院床铺上的接触压力对预防压疮和实现患者实时评估至关重要。现有方法虽可预测压力图，但常缺乏物理合理性，限制了临床可靠性。本文提出一种框架，通过有信息的潜在空间（ILS）和权重优化损失（WOL）结合条件生成建模，生成高保真、物理一致的压力估计。研究还应用基于扩散的条件布朗运动桥扩散模型（BBDM），并提出其潜在形式——潜空间布朗运动桥扩散模型（LBBDM）以适配卧姿压力合成。实验表明，所提方法在物理合理性与性能上均优于基线：带ILS的BBDM生成细节丰富地图但计算成本高、推理时间长；而LBBDM提供更快推理且性能相当。整体方法支持临床环境中非侵入式、基于视觉的实时患者监测。
原文摘要 · Abstract (English)
Monitoring contact pressure in hospital beds is essential for preventing pressure ulcers and enabling real-time patient assessment. Current methods can predict pressure maps but often lack physical plausibility, limiting clinical reliability. This work proposes a framework that enhances plausibility via Informed Latent Space (ILS) and Weight Optimization Loss (WOL) with conditional generative modeling to produce high-fidelity, physically consistent pressure estimates. This study also applies diffusion based conditional Brownian Bridge Diffusion Model (BBDM) and proposes training strategy for its latent counterpart Latent Brownian Bridge Diffusion Model (LBBDM) tailored for pressure synthesis in lying postures. Experiment results shows proposed method improves physical plausibility and performance over baselines: BBDM with ILS delivers highly detailed maps at higher computational cost and large inference time, whereas LBBDM provides faster inference with competitive performance. Overall, the approach supports non-invasive, vision-based, real-time patient monitoring in clinical environments.
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