PressurePose+PressureNet：压力传感垫实现3D人体姿态估计，座舱OOP检测新方案

论文信息

项目 内容
论文标题 Bodies at Rest: 3D Human Pose and Shape Estimation From a Pressure Image Using Synthetic Data
作者 Mackenzie E. McDermott, Henry M. Clever, Roni O. Nasim
会议 CVPR 2020
DOI 10.1109/cvpr42600.2020.00625
OpenReview https://openreview.net/forum?id=7x6AkzZaFck
代码/数据 PressurePose Dataset (206K synthetic pressure images)

核心创新

首次提出完全基于合成数据训练的3D人体姿态和形状估计方法，从压力传感垫图像直接推断3D人体网格。

特性 说明
传感器 压力传感阵列 (pressure mat)
输出 3D人体网格 (mesh)
训练数据 206K 合成压力图像
测试数据 1,051 真实压力图像 (20人)
关键突破 纯合成训练→真实域迁移成功

方法详解

1. 两阶段物理仿真管线

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"""
PressurePose 数据生成管线
两阶段: 刚体→软体仿真

依赖: pybullet, trimesh, pytorch3d
数据集: 206K 合成压力图像
"""

import numpy as np
from dataclasses import dataclass
from typing import Tuple

@dataclass
class PressureMatConfig:
    """压力传感垫配置"""
    mat_width: int = 192       # 传感单元数 (水平)
    mat_height: int = 96       # 传感单元数 (垂直)
    mat_physical_width: float = 0.48  # 物理宽度 (m)
    mat_physical_height: float = 0.24  # 物理高度 (m)
    cell_size: float = 0.0025  # 每个传感单元物理尺寸 (m)
    force_range: float = 0.0   # 测力范围 (N)
    max_force: float = 100.0   # 最大测力 (N)
    sampling_rate: int = 10    # 采样率 (Hz)


class Stage1_RigidBodySimulator:
    """
    阶段1: 刚体物理仿真
    
    目标: 找到人体在软床/座椅上的稳定静止姿态
    - 模拟关节化刚体人体模型
    - 与软床和压力垫交互
    - 使用物理引擎找到稳定平衡姿态
    """
    
    def __init__(self, config: PressureMatConfig):
        self.config = config
        # 人体模型参数 (SMPL-X)
        self.num_joints = 55     # SMPL-X 关芽数
        self.num_vertices = 10475  # SMPL-X 顶点数
        
    def generate_pose(self, 
                      gender: str = 'neutral',
                      posture: str = 'supine',
                      limb_variation: float = 0.3) -> dict:
        """
        生成一个稳定的静止姿态
        
        Args:
            gender: 'male', 'female', 'neutral'
            posture: 'supine'(仰卧), 'lateral'(侧卧), 'prone'(俯卧)
            limb_variation: 四肢位置变化幅度 (0-1)
        
        Returns:
            pose_dict: 包含关节角度、顶点、接触点
        """
        # 基础姿态模板
        base_poses = {
            'supine': self._supine_template(),
            'lateral': self._lateral_template(),
            'prone': self._prone_template(),
        }
        
        # 随机扰动四肢
        pose = base_poses[posture].copy()
        if limb_variation > 0:
            # 手臂: 肩关节旋转
            pose[16:19] += np.random.uniform(
                -0.5, 0.5, 3
            ) * limb_variation
            pose[19:22] += np.random.uniform(
                -0.5, 0.5, 3
            ) * limb_variation
            
            # 腿部: 髋关节和膝关节
            pose[1:4] += np.random.uniform(
                -0.3, 0.3, 3
            ) * limb_variation
            pose[4:7] += np.random.uniform(
                -0.3, 0.3, 3
            ) * limb_variation
        
        # 物理仿真找到稳定姿态
        stable_pose = self._physics_relax(pose)
        
        return {
            'pose': stable_pose,
            'gender': gender,
            'posture': posture,
        }
    
    def _supine_template(self):
        """仰卧模板"""
        pose = np.zeros(55 * 3)  # SMPL-X pose参数
        # 头部略偏
        pose[12] = 0.1
        # 手臂自然放置
        pose[16] = -0.3  # 左肩外旋
        pose[19] = 0.3   # 右肩外旋
        return pose
    
    def _lateral_template(self):
        """侧卧模板"""
        pose = np.zeros(55 * 3)
        pose[3] = 1.5  # 躯干旋转90度
        pose[12] = 0.2  # 颈部
        return pose
    
    def _prone_template(self):
        """俯卧模板"""
        pose = np.zeros(55 * 3)
        pose[3] = 3.14  # 躯干旋转180度
        return pose
    
    def _physics_relax(self, pose, max_iter=1000):
        """物理引擎松弛，找到稳定姿态"""
        # 简化版: 迭代降低势能
        for _ in range(max_iter):
            # 模拟重力、接触力
            # 调整关节角度直到合力<阈值
            pass
        return pose


class Stage2_SoftBodySimulator:
    """
    阶段2: 软体仿真生成压力图像
    
    输入: 阶段1的稳定姿态
    输出: 模拟压力传感垫图像 (192×96)
    """
    
    def __init__(self, config: PressureMatConfig):
        self.config = config
        self.mat_shape = (config.mat_height, config.mat_width)
        
    def generate_pressure_image(self, pose_dict: dict) -> np.ndarray:
        """
        从3D人体姿态生成压力图像
        
        Args:
            pose_dict: 包含姿态、性别等
            
        Returns:
            pressure_img: (H, W) 压力值矩阵 0-max_force
        """
        # 1. 从SMPL-X参数生成人体网格
        mesh_vertices = self._get_mesh_vertices(pose_dict)
        
        # 2. 软体变形计算接触面积
        contact_points = self._compute_contact(mesh_vertices)
        
        # 3. 模拟压力分布
        pressure_img = self._simulate_pressure(contact_points, mesh_vertices)
        
        return pressure_img
    
    def _get_mesh_vertices(self, pose_dict):
        """从SMPL-X参数获取顶点"""
        # 简化: 返回N×3顶点坐标
        num_verts = 10475
        return np.random.randn(num_verts, 3) * 0.5
    
    def _compute_contact(self, vertices):
        """计算与压力垫的接触点"""
        # 找z坐标最低的顶点
        z_threshold = np.percentile(vertices[:, 2], 5)
        contact_mask = vertices[:, 2] < z_threshold
        return vertices[contact_mask]
    
    def _simulate_pressure(self, contact_points, all_vertices):
        """软体压力仿真"""
        pressure_img = np.zeros(self.mat_shape)
        
        for point in contact_points:
            # 将3D点映射到压力垫坐标
            x, y, z = point
            mat_x = int((x + self.config.mat_physical_width/2) / 
                        self.config.cell_size)
            mat_y = int((y + self.config.mat_physical_height/2) / 
                         self.config.cell_size)
            
            if 0 <= mat_x < self.config.mat_width and \
               0 <= mat_y < self.config.mat_height:
                # 高斯扩散
                sigma = 3
                for dx in range(-sigma*2, sigma*2+1):
                    for dy in range(-sigma*2, sigma*2+1):
                        xi, yi = mat_x+dx, mat_y+dy
                        if 0 <= xi < self.config.mat_width and \
                           0 <= yi < self.config.mat_height:
                            pressure_img[yi, xi] += \
                                np.exp(-(dx**2+dy**2)/(2*sigma**2))
        
        # 归一化到力范围
        pressure_img = np.clip(pressure_img, 0, self.config.max_force)
        return pressure_img


class PressurePoseDataset:
    """PressurePose 数据集"""
    
    def __init__(self, root_dir='/data/PressurePose'):
        self.root = root_dir
        self.config = PressureMatConfig()
        
        # 数据集统计
        self.total_images = 206_000   # 206K合成
        self.train_split = 184_000    # 训练集
        self.test_split = 22_000      # 测试集
        self.real_images = 1_051      # 真实测试集
        self.real_subjects = 20        # 20个真实受试者
        
    def get_stats(self):
        return {
            'total_synthetic': self.total_images,
            'train': self.train_split,
            'test': self.test_split,
            'real_test': self.real_images,
            'real_subjects': self.real_subjects,
            'postures': ['supine', 'lateral', 'prone', 'hands_behind_head'],
        }


if __name__ == "__main__":
    config = PressureMatConfig()
    
    print("=" * 60)
    print("PressurePose 数据生成管线")
    print("=" * 60)
    
    print(f"\n压力垫配置:")
    print(f"  尺寸: {config.mat_width}×{config.mat_height} 传感单元")
    print(f"  物理尺寸: {config.mat_physical_width}m × {config.mat_physical_height}m")
    print(f"  单元大小: {config.cell_size*1000:.1f}mm")
    
    # 阶段1: 刚体仿真
    stage1 = Stage1_RigidBodySimulator(config)
    pose = stage1.generate_pose(gender='neutral', posture='supine')
    print(f"\n阶段1 - 刚体仿真:")
    print(f"  姿态: {pose['posture']}")
    print(f"  关节数: {stage1.num_joints}")
    
    # 阶段2: 软体仿真
    stage2 = Stage2_SoftBodySimulator(config)
    pressure_img = stage2.generate_pressure_image(pose)
    print(f"\n阶段2 - 软体仿真:")
    print(f"  压力图像: {pressure_img.shape}")
    print(f"  非零单元: {np.count_nonzero(pressure_img)}/{pressure_img.size}")
    print(f"  最大压力: {pressure_img.max():.2f} N")
    print(f"  总压力: {pressure_img.sum():.2f} N")

2. PressureNet 架构

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"""
PressureNet: 从压力图像推断3D人体网格

架构: 两阶段(Mod1+Mod2) + 压力图重建(PMR)网络

输入: 压力图像 (96×192) + 性别
输出: 3D人体网格 (10475顶点×3坐标)

关键创新: PMR网络强制一致性
"""

import torch
import torch.nn as nn

class PressureMapReconstruction(nn.Module):
    """
    PMR (Pressure Map Reconstruction) 网络
    
    核心创新: 从预测的3D网格重建压力图
    与输入压力图比较，强制一致性
    
    作用: 防止模型将接触点误判为错误姿态
    """
    
    def __init__(self, hidden_dim=128):
        super().__init__()
        
        # 从3D顶点特征预测压力图
        self.vertex_to_pressure = nn.Sequential(
            nn.Linear(10475 * 3, hidden_dim * 4),
            nn.ReLU(),
            nn.Linear(hidden_dim * 4, hidden_dim * 2),
            nn.ReLU(),
            nn.Linear(hidden_dim * 2, 96 * 192),  # 压力图尺寸
            nn.Sigmoid()
        )
        
    def forward(self, predicted_vertices):
        """
        Args:
            predicted_vertices: (B, 10475, 3) 预测的3D顶点
            
        Returns:
            reconstructed_pressure: (B, 1, 96, 192) 重建的压力图
        """
        B = predicted_vertices.shape[0]
        flat = predicted_vertices.reshape(B, -1)
        pressure = self.vertex_to_pressure(flat)
        return pressure.reshape(B, 1, 96, 192)


class PressureNetModule(nn.Module):
    """
    PressureNet 单个模块 (Mod1 或 Mod2)
    
    输入: 压力图 (+ 性别one-hot)
    输出: 3D人体顶点 + 重建的压力图
    """
    
    def __init__(self, input_channels=1, num_vertices=10475):
        super().__init__()
        
        # 编码器: 从压力图提取特征
        self.encoder = nn.Sequential(
            nn.Conv2d(input_channels, 32, 3, stride=2, padding=1),
            nn.BatchNorm2d(32),
            nn.ReLU(),
            
            nn.Conv2d(32, 64, 3, stride=2, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            
            nn.Conv2d(64, 128, 3, stride=2, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(),
            
            nn.Conv2d(128, 256, 3, stride=2, padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU(),
            
            nn.AdaptiveAvgPool2d(1),
        )
        
        # 解码器: 从特征预测3D顶点
        self.decoder = nn.Sequential(
            nn.Linear(256 + 3, 512),  # +3 for gender one-hot
            nn.ReLU(),
            nn.Linear(512, 1024),
            nn.ReLU(),
            nn.Linear(1024, num_vertices * 3)
        )
        
        # PMR网络
        self.pmr = PressureMapReconstruction()
        
    def forward(self, pressure_img, gender):
        """
        Args:
            pressure_img: (B, 1, 96, 192)
            gender: (B, 3) one-hot
            
        Returns:
            vertices: (B, 10475, 3)
            reconstructed_pressure: (B, 1, 96, 192)
        """
        # 编码
        feat = self.encoder(pressure_img)  # (B, 256, 1, 1)
        feat = feat.flatten(1)  # (B, 256)
        feat = torch.cat([feat, gender], dim=1)  # (B, 259)
        
        # 解码3D顶点
        vertices = self.decoder(feat)  # (B, 10475*3)
        vertices = vertices.reshape(-1, 10475, 3)
        
        # PMR: 从顶点重建压力图
        reconstructed_pressure = self.pmr(vertices)
        
        return vertices, reconstructed_pressure


class PressureNet(nn.Module):
    """
    PressureNet 完整模型
    
    Mod1: 粗估计 (coarse)
    Mod2: 精细化 (refine) - 使用Mod1的重建压力图作为额外输入
    """
    
    def __init__(self):
        super().__init__()
        self.mod1 = PressureNetModule(input_channels=1)
        self.mod2 = PressureNetModule(input_channels=2)  # 原始+Mod1重建
        
    def forward(self, pressure_img, gender):
        # Mod1: 粗估计
        verts1, recon_pressure1 = self.mod1(pressure_img, gender)
        
        # Mod2: 精细化
        mod2_input = torch.cat([pressure_img, recon_pressure1], dim=1)
        verts2, recon_pressure2 = self.mod2(mod2_input, gender)
        
        return {
            'vertices_coarse': verts1,
            'vertices_refined': verts2,
            'recon_pressure_1': recon_pressure1,
            'recon_pressure_2': recon_pressure2,
        }


# ========== 测试 ==========
if __name__ == "__main__":
    device = 'cuda' if torch.cuda.is_available() else 'cpu'
    
    model = PressureNet().to(device)
    
    # 模拟输入
    pressure_img = torch.randn(2, 1, 96, 192).to(device)
    gender = torch.eye(3)[:2].to(device)  # 2个样本
    
    output = model(pressure_img, gender)
    
    print("PressureNet 输出:")
    for k, v in output.items():
        print(f"  {k}: {v.shape}")
    
    # 损失函数
    def pressurenet_loss(output, target_pressure, target_vertices):
        """
        多任务损失:
        1. 顶点L2损失 (Mod1 + Mod2)
        2. 压力图重建损失 (PMR一致性)
        """
        loss = 0
        
        # 顶点损失
        if target_vertices is not None:
            loss += 0.5 * torch.nn.functional.mse_loss(
                output['vertices_coarse'], target_vertices
            )
            loss += 1.0 * torch.nn.functional.mse_loss(
                output['vertices_refined'], target_vertices
            )
        
        # PMR 一致性损失
        loss += 0.3 * torch.nn.functional.mse_loss(
            output['recon_pressure_1'], target_pressure
        )
        loss += 0.3 * torch.nn.functional.mse_loss(
            output['recon_pressure_2'], target_pressure
        )
        
        return loss
    
    # 计算损失
    loss = pressurenet_loss(output, pressure_img, None)
    print(f"\n初始损失: {loss.item():.4f}")
    
    # 模型大小
    params = sum(p.numel() for p in model.parameters())
    print(f"模型参数量: {params/1e6:.1f}M")
    
    # 性能指标 (来自论文)
    print("\n论文报告性能:")
    print(f"  MPJPE (合成测试): 11.18 cm")
    print(f"  3DVPE (合成测试): 3.94 cm")
    print(f"  3DVPE (真实-规定姿态): 4.99 cm")
    print(f"  3DVPE (真实-自选姿态): 3.93 cm")

3. 消融实验：PMR的重要性

配置 MPJPE (cm) 3DVPE (cm) 说明
PressureNet (完整) 11.18 3.94 Mod1+Mod2+PMR
无 PMR 12.28 4.38 MPJPE↑1.1, 3DVPE↑0.44
仅 Mod1 14.52 5.87 无精细化
无性别输入 11.85 4.21 性别信息有助

IMS OOP 应用启示

1. 座椅压力垫 → OOP检测

2. OOP检测方案对比

方案 优势 劣势 成本 推荐度
摄像头3D姿态 精度高，信息丰富 隐私问题，遮挡 中 ★★★★
压力垫+PressureNet 无隐私问题，穿透毯子 精度中等，需加装传感器 低 ★★★★★
雷达3D感知 穿透力强 分辨率低 中高 ★★★
多传感器融合 最可靠 成本高，复杂 高 ★★★★★

3. 座椅压力垫硬件选型

参数 推荐值 说明
传感单元数 192×96 (18,432) 论文配置
物理尺寸 480mm × 240mm 标准座椅坐垫
单元尺寸 2.5mm × 2.5mm 足够分辨率
测力范围 0-100N 成人坐压范围
采样率 10-30Hz OOP不需要高频
通信接口 CAN-FD / LIN 车规级
工作温度 -40°C ~ +85°C 车规级
厂商 型号 参数 价格
Tekscan BPMS 2,064传感点, 汽车版 $3,000+ (研发)
XSENSOR PX100 10,000+传感点 $2,000+ (研发)
自研方案 压阻阵列 定制FPC 量产<$50/座

4. 座舱OOP检测代码

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import numpy as np
from scipy.spatial.distance import cdist

class CabinOOPDetector:
    """
    基于压力垫的座舱OOP检测器
    
    使用PressureNet预测3D姿态后，
    通过关节角度和躯干方向判断OOP状态
    """
    
    # OOP状态定义 (基于Euro NCAP)
    OOP_STATES = {
        'normal': {'level': 0, 'description': '正常坐姿'},
        'forward_lean': {'level': 1, 'description': '前倾 (安全带可能滑落)'},
        'side_lean': {'level': 1, 'description': '侧倾 (碰撞保护降低)'},
        'reclined': {'level': 1, 'description': '后仰过度 (安全带位置不当)'},
        'feet_dashboard': {'level': 2, 'description': '脚放在仪表板 (严重OOP)'},
        'out_of_position': {'level': 2, 'description': '严重异常姿态'},
    }
    
    def __init__(self, pressure_model):
        self.model = pressure_model  # 预训练PressureNet
        
    def detect(self, pressure_img, gender):
        """
        从压力图像检测OOP状态
        
        Args:
            pressure_img: (96, 192) 压力矩阵
            gender: 'M'/'F'/'N'
            
        Returns:
            oop_state: str, OOP状态
            confidence: float, 置信度
            risk_level: int, 风险等级 0/1/2
        """
        # 1. 预测3D姿态
        vertices = self._predict_pose(pressure_img, gender)
        
        # 2. 提取关键关节
        joints = self._extract_joints(vertices)
        
        # 3. 计算OOP指标
        metrics = self._compute_oop_metrics(joints)
        
        # 4. 分类
        oop_state = self._classify(metrics)
        
        return oop_state, metrics
    
    def _predict_pose(self, pressure_img, gender):
        """运行PressureNet"""
        # ... (使用上面的PressureNet模型)
        pass
    
    def _extract_joints(self, vertices):
        """从SMPL-X顶点提取关键关节"""
        # 关键关节索引 (SMPL-X)
        joint_indices = {
            'head': 12,        # 头部
            'neck': 12,
            'left_shoulder': 16,
            'right_shoulder': 17,
            'spine': 0,
            'left_hip': 1,
            'right_hip': 2,
            'left_knee': 4,
            'right_knee': 5,
        }
        return {name: vertices[idx] for name, idx in joint_indices.items()}
    
    def _compute_oop_metrics(self, joints):
        """计算OOP判断指标"""
        metrics = {}
        
        # 躯干角度 (从脊柱方向)
        spine_vec = joints['spine'] - joints['head']
        metrics['torso_angle'] = np.arctan2(
            spine_vec[0], spine_vec[2]
        )  # 与垂直方向的角度
        
        # 前倾检测
        metrics['forward_lean'] = joints['head'][2] > joints['spine'][2] + 0.1
        
        # 侧倾检测
        metrics['side_lean'] = abs(joints['left_shoulder'][0] - 
                                   joints['right_shoulder'][0]) > 0.3
        
        # 压力分布不对称
        left_pressure = np.sum(pressure_img[:, :96])
        right_pressure = np.sum(pressure_img[:, 96:])
        metrics['pressure_asymmetry'] = abs(
            left_pressure - right_pressure
        ) / (left_pressure + right_pressure + 1e-6)
        
        return metrics
    
    def _classify(self, metrics):
        """分类OOP状态"""
        if metrics['torso_angle'] > 0.5:
            return 'forward_lean'
        elif metrics['pressure_asymmetry'] > 0.4:
            return 'side_lean'
        elif metrics['torso_angle'] < -0.3:
            return 'reclined'
        else:
            return 'normal'


if __name__ == "__main__":
    # 模拟OOP检测
    np.random.seed(42)
    
    # 正常坐姿压力图 (居中分布)
    normal_pressure = np.zeros((96, 192))
    yy, xx = np.meshgrid(range(96), range(192), indexing='ij')
    normal_pressure = np.exp(-((xx-96)**2 + (yy-48)**2) / (2*30**2)) * 50
    
    # 前倾坐姿压力图 (前部集中)
    forward_pressure = np.exp(-((xx-96)**2 + (yy-20)**2) / (2*20**2)) * 70
    
    # 侧倾压力图 (左半部集中)
    side_pressure = np.zeros((96, 192))
    side_pressure[:, :120] = np.exp(-((xx[:120]-60)**2 + (yy-48)**2) / (2*25**2)) * 60
    
    print("OOP检测模拟:")
    for name, pressure in [("正常", normal_pressure), 
                           ("前倾", forward_pressure),
                           ("侧倾", side_pressure)]:
        left = np.sum(pressure[:, :96])
        right = np.sum(pressure[:, 96:])
        asym = abs(left - right) / (left + right + 1e-6)
        centroid_y = np.sum(pressure * np.arange(96)[:, None]) / (np.sum(pressure) + 1e-6)
        
        print(f"\n{name}:")
        print(f"  压力不对称: {asym:.2f} (>0.4=侧倾)")
        print(f"  压力中心Y: {centroid_y:.1f} (<40=前倾, >56=后仰)")

5. Euro NCAP OOP要求对照

Euro NCAP OOP场景 压力垫可检测 说明
OOP-01 前倾坐姿 ✅ 压力中心前移
OOP-02 侧倾坐姿 ✅ 左右压力不对称
OOP-03 后仰过度 ✅ 压力中心后移
OOP-04 脚放仪表板 ❌ 需摄像头补充
OOP-05 儿童座椅异常 ⚠️ 需要特定训练
OOP-06 跪姿 ✅ 压力分布异常

参考文献

  1. McDermott, M.E., Clever, H.M., Nasim, R.O. “Bodies at Rest: 3D Human Pose and Shape Estimation From a Pressure Image Using Synthetic Data.” CVPR 2020. DOI: 10.1109/cvpr42600.2020.00625
  2. PressurePose Dataset: 206K synthetic pressure images with 3D poses
  3. Euro NCAP 2026 OMS Protocol - OOP detection requirements

PressurePose的核心价值在于证明了”纯合成数据→真实域迁移”的可行性。对于IMS OOP检测，座椅压力垫方案成本低、无隐私问题、可穿透毯子，是摄像头方案的强力补充。建议采购Tekscan研发版进行验证，量产采用自研压阻阵列方案。