I am interested in obtaining joints from the inferred SMPL image and visualize it similar to described in README of this project: https://github.com/gulvarol/smplpytorch.
I changed
|
pred_vertices, pred_vertices_smpl, pred_camera, _, _ = model(norm_img.cuda()) |
to
pred_vertices, pred_vertices_smpl, pred_camera, smpl_pose, smpl_shape = model(...) to get
smpl_pose (of shape
torch.Size([1, 24, 3, 3])). Then I just flattened it by doing
smpl_pose.cpu().data.numpy()[:, :, :, -1].flatten('C').reshape(1, -1) and used the resulting
(1, 72) pose params as input in
pose_params variable of smplpytorch demo.
The resulting visualization doesn't look correct to me. Is this the right approach? Perhaps there is an easier way to do what I am doing.
I am interested in obtaining joints from the inferred SMPL image and visualize it similar to described in README of this project: https://github.com/gulvarol/smplpytorch.
I changed
GraphCMR/demo.py
Line 118 in 4e57dca
pred_vertices, pred_vertices_smpl, pred_camera, smpl_pose, smpl_shape = model(...)to getsmpl_pose(of shapetorch.Size([1, 24, 3, 3])). Then I just flattened it by doingsmpl_pose.cpu().data.numpy()[:, :, :, -1].flatten('C').reshape(1, -1)and used the resulting(1, 72)pose params as input in pose_params variable of smplpytorch demo.The resulting visualization doesn't look correct to me. Is this the right approach? Perhaps there is an easier way to do what I am doing.