\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...
1], # x 方向平移 pred_camera[:, 2], # y 方向平移 2 * 5000. / (224. * pred_camera[:, 0] + 1e-9)], dim=-1) # z 方向平移 batch_size = pred_joints.shape[0] camera_center = torch.zeros(batch_size, 2) rotation = torch.eye(3).unsqueeze(0).expand(batch_size, -1, ...
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...
CIRCUMFERENCESare defined with landmarks and joints - the measurement is found by cutting the body model with the plane defined by a point (landmark point) and normal ( vector connecting the two joints) If the measurement is aCIRCUMFERENCE, a possible issue that arises is that the plane cut...
rotationand{body,eyes,jaw}joints,24parametersfor P whereB(β;S)=|β|βSistheshapeblendshapethelowerdimensionalhandposePCAspace,10forsub- Sn=1nn function,βarelinearshapecoefficients,|β|istheirnumber,jectshapeand10forthefacialexpressions.Additionally ...
python examples/demo.py --model-folder $SMPLX_FOLDER --plot-joints=True --gender="neutral" Depending on which model is loaded for your project, i.e. SMPL-X or SMPL+H or SMPL, please cite the most relevant work below, listed in the same order: ...
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), ...
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]...