论文标题
休息时的身体：使用合成数据从压力图像中进行3D人姿势和形状估计
Bodies at Rest: 3D Human Pose and Shape Estimation from a Pressure Image using Synthetic Data
论文作者
论文摘要
人们在床上休息。 3D人的姿势和这种活动的形状估计将具有许多有益的应用，但是视线的感知因床上用品的阻塞而复杂。压力感应垫是一种有前途的选择，但是训练数据在大规模收集方面具有挑战性。我们描述了一种基于物理学的方法，该方法模拟了带有压力传感垫的床中的静止物体，并呈现压力点，这是一个具有206K压力图像的合成数据集，具有3D人体姿势和形状。我们还提出了Perspurenet，这是一个深度学习模型，估计了压力图像和性别的人类姿势和形状。 Perperturenet结合了压力图重建（PMR）网络，该网络对压力图像产生建模，以促进估计的3D身体模型和压力图像输入之间的一致性。在我们的评估中，Passurenet在参与者的实际数据中表现出色，即使它仅接受了合成数据的培训。当我们消融PMR网络时，性能大大下降。
People spend a substantial part of their lives at rest in bed. 3D human pose and shape estimation for this activity would have numerous beneficial applications, yet line-of-sight perception is complicated by occlusion from bedding. Pressure sensing mats are a promising alternative, but training data is challenging to collect at scale. We describe a physics-based method that simulates human bodies at rest in a bed with a pressure sensing mat, and present PressurePose, a synthetic dataset with 206K pressure images with 3D human poses and shapes. We also present PressureNet, a deep learning model that estimates human pose and shape given a pressure image and gender. PressureNet incorporates a pressure map reconstruction (PMR) network that models pressure image generation to promote consistency between estimated 3D body models and pressure image input. In our evaluations, PressureNet performed well with real data from participants in diverse poses, even though it had only been trained with synthetic data. When we ablated the PMR network, performance dropped substantially.
