\mathcal{P}=\left[\mathbf{P}_{1}, \ldots, \mathbf{P}_{9 K}\right] \in \mathbb{R}^{3 N \times 9 K}: 所有207个姿势混合形状组成的矩阵 (由姿势引起位移的正交主成分) \mathcal{J}: 将rest vertices转换成rest joints 的矩阵(获取T pose的关节点坐标的矩阵)[完成顶点到关节的转化] 2.3...
W∈RN×KW∈RN×K: blend weights. 一组混合权重,BS/QBS混合权重矩阵,即关节点对顶点的影响权重 (第几个顶点受哪些关节点的影响且权重分别为多少) (a set of blend weights) J:J:Joint regressor matrix. 将rest vertices转换成rest joints的矩阵(获取T pose的关节点坐标的矩阵)[完成顶点到关节的转化] 训练...
2023.7.4更新,3d关节点恢复姿态也可参考[17]里的IK for 3D joints,IK for mocap markers。 七.参考 [1]SMPL: A Skinned Multi-Person Linear Model [2]Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image [3]人体模型介绍 - SMPL [4]徐土豆:人体动作捕捉与SMPL模型 ...
θ是代表⼈体整体运动位姿和24个关节相对⾓度的75(24*3+3;每个关节点3个⾃由度，再加上3个根节点)个参数，是⼀个3K-D的vector（代表pose，其中K为⾻架节点数，3是每个关节具有的3个⾃由度）。β参数是Shape Blend Pose参数，可以通过10个增量模板控制⼈体形状变化：具体⽽⾔：每个参数控制...
cat([pred_cam, pose, pred_shape], dim=1), 'verts' : pred_vertices, 'kp_2d' : pred_keypoints_2d, 'kp_3d' : pred_joints, 'rotmat' : pred_rotmat }] return output 我们首先看看__init__部分。 #1. 预测参数维度定义。10个shape参数和3个相机参数(平移、旋转、缩放)。pose的维度是 24\...
importpicklewithopen(model_path,'rb')asf:smpl=pickle.load(f,encoding='latin1')'J_regressor_prior':[24,6890],scipy.sparse.csc.csc_matrix# 面部'f':[13776,3],numpy.ndarray# regressor array that is used to calculate the 3d joints from the position of the vertices'J_regressor':[24,6890]...
rotationand{body,eyes,jaw}joints,24parametersfor P whereB(β;S)=|β|βSistheshapeblendshapethelowerdimensionalhandposePCAspace,10forsub- Sn=1nn function,βarelinearshapecoefficients,|β|istheirnumber,jectshapeand10forthefacialexpressions.Additionally Sn∈R3Nareorthonormalpriplecomponentsofver-therearesep...
smpl_pose (of shape torch.Size([1, 24, 3, 3])) is the SMPL pose parameters expressed as a rotation matrix. You need to make a transformation from rotation matrix to axis-angle representation which is (72,1). You can use Rodrigues formula to do it, as claimed in the paper: Get ...
Kostrikov and Gall [24] combine regression forests and a 3D pictorial model to regress 3D joints. Ionescu et al. [17] train a method to predict 3D pose from images by first predicting body part labels; their results on Human3.6M are good but they do not test on complex images where ...
# joints location self.J = self.J_regressor.dot(v_shaped) pose_cube = self.pose.reshape((-1, 1, 3)) # rotation matrix for each joint self.R = self.rodrigues(pose_cube) I_cube = np.broadcast_to( np.expand_dims(np.eye(3), axis=0), ...