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add normalization function, and some minor modification
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@zhan-xu
zhan-xu committed Aug 11, 2020
1 parent 0a25c4d commit 95ac31c
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Showing 7 changed files with 10,511 additions and 35 deletions.
9 changes: 5 additions & 4 deletions maya_save_fbx.py
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Original file line number Diff line number Diff line change
Expand Up @@ -81,11 +81,12 @@ def getGeometryGroups():


if __name__ == '__main__':
model_id = 17872
#model_id = "17872"
model_id = "smith"
print(model_id)
obj_name = 'D:\\{:d}_ori.obj'.format(model_id)
info_name = 'D:\\{:d}_ori_rig.txt'.format(model_id)
out_name = 'D:\\{:d}.fbx'.format(model_id)
obj_name = 'D:\\{:s}_ori.obj'.format(model_id)
info_name = 'D:\\{:s}_ori_rig.txt'.format(model_id)
out_name = 'D:\\{:s}.fbx'.format(model_id)

# import obj
cmds.file(new=True,force=True)
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80 changes: 53 additions & 27 deletions quick_start.py
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Original file line number Diff line number Diff line change
Expand Up @@ -36,6 +36,20 @@
from models.SKINNING import SKINNET


def normalize_obj(mesh_v):
dims = [max(mesh_v[:, 0]) - min(mesh_v[:, 0]),
max(mesh_v[:, 1]) - min(mesh_v[:, 1]),
max(mesh_v[:, 2]) - min(mesh_v[:, 2])]
scale = 1.0 / max(dims)
pivot = np.array([(min(mesh_v[:, 0]) + max(mesh_v[:, 0])) / 2, min(mesh_v[:, 1]),
(min(mesh_v[:, 2]) + max(mesh_v[:, 2])) / 2])
mesh_v[:, 0] -= pivot[0]
mesh_v[:, 1] -= pivot[1]
mesh_v[:, 2] -= pivot[2]
mesh_v *= scale
return mesh_v, pivot, scale


def create_single_data(mesh_filaname):
"""
create input data for the network. The data is wrapped by Data structure in pytorch-geometric library
Expand All @@ -47,6 +61,10 @@ def create_single_data(mesh_filaname):
mesh_vn = np.asarray(mesh.vertex_normals)
mesh_f = np.asarray(mesh.triangles)

mesh_v, translation_normalize, scale_normalize = normalize_obj(mesh_v)
mesh_normalized = o3d.geometry.TriangleMesh(vertices=o3d.utility.Vector3dVector(mesh_v), triangles=o3d.utility.Vector3iVector(mesh_f))
o3d.io.write_triangle_mesh(mesh_filename.replace("_remesh.obj", "_normalized.obj"), mesh_normalized)

# vertices
v = np.concatenate((mesh_v, mesh_vn), axis=1)
v = torch.from_numpy(v).float()
Expand All @@ -72,12 +90,13 @@ def create_single_data(mesh_filaname):
batch = torch.from_numpy(batch).long()

# voxel
os.system("./binvox -d 88 -pb " + mesh_filaname)
with open(mesh_filaname.replace('.obj', '.binvox'), 'rb') as fvox:
if not os.path.exists(mesh_filaname.replace('_remesh.obj', '_normalized.binvox')):
os.system("./binvox -d 88 -pb " + mesh_filaname.replace("_remesh.obj", "_normalized.obj"))
with open(mesh_filaname.replace('_remesh.obj', '_normalized.binvox'), 'rb') as fvox:
vox = binvox_rw.read_as_3d_array(fvox)

data = Data(x=v[:, 3:6], pos=v[:, 0:3], tpl_edge_index=tpl_e, geo_edge_index=geo_e, batch=batch)
return data, vox, surface_geodesic
return data, vox, surface_geodesic, translation_normalize, scale_normalize


def predict_joints(input_data, vox, joint_pred_net, threshold, bandwidth=None, mesh_filename=None):
Expand Down Expand Up @@ -107,7 +126,7 @@ def predict_joints(input_data, vox, joint_pred_net, threshold, bandwidth=None, m

#img = draw_shifted_pts(mesh_filename, y_pred_np, weights=attn_pred_np)
if bandwidth is None:
bandwidth = bandwidth_pred
bandwidth = bandwidth_pred.item()
y_pred_np = meanshift_cluster(y_pred_np, bandwidth, attn_pred_np, max_iter=40)
#img = draw_shifted_pts(mesh_filename, y_pred_np, weights=attn_pred_np)

Expand Down Expand Up @@ -367,41 +386,48 @@ def tranfer_to_ori_mesh(filename_ori, filename_remesh, pred_rig):
# To process other input characters, please first try the learned bandwidth (0.429 in the provided model), and the default threshold 1e-5.
# We also use these two default parameters for processing all test models in batch.

model_id, bandwidth, threshold = 17872, 0.045, 0.75e-5
#model_id, bandwidth, threshold = 8210, 0.05, 1e-5
#model_id, bandwidth, threshold = 8330, 0.05, 0.8e-5
#model_id, bandwidth, threshold = 9477, 0.043, 2.5e-5
#model_id, bandwidth, threshold = 17364, 0.058, 0.3e-5
#model_id, bandwidth, threshold = 15930, 0.055, 0.4e-5
#model_id, bandwidth, threshold = 8333, 0.04, 2e-5
#model_id, bandwidth, threshold = 8338, 0.052, 0.9e-5
#model_id, bandwidth, threshold = 3318, 0.03, 0.92e-5
#model_id, bandwidth, threshold = 15446, 0.032, 0.58e-5
#model_id, bandwidth, threshold = 1347, 0.062, 3e-5
#model_id, bandwidth, threshold = 11814, 0.06, 0.6e-5
#model_id, bandwidth, threshold = 2982, 0.045, 0.3e-5
#model_id, bandwidth, threshold = 2586, 0.05, 0.6e-5
#model_id, bandwidth, threshold = 8184, 0.05, 0.4e-5
#model_id, bandwidth, threshold = 9000, 0.035, 0.16e-5
#model_id, bandwidth, threshold = "smith", None, 1e-5
model_id, bandwidth, threshold = "17872", 0.045, 0.75e-5
#model_id, bandwidth, threshold = "8210", 0.05, 1e-5
#model_id, bandwidth, threshold = "8330", 0.05, 0.8e-5
#model_id, bandwidth, threshold = "9477", 0.043, 2.5e-5
#model_id, bandwidth, threshold = "17364", 0.058, 0.3e-5
#model_id, bandwidth, threshold = "15930", 0.055, 0.4e-5
#model_id, bandwidth, threshold = "8333", 0.04, 2e-5
#model_id, bandwidth, threshold = "8338", 0.052, 0.9e-5
#model_id, bandwidth, threshold = "3318", 0.03, 0.92e-5
#model_id, bandwidth, threshold = "15446", 0.032, 0.58e-5
#model_id, bandwidth, threshold = "1347", 0.062, 3e-5
#model_id, bandwidth, threshold = "11814", 0.06, 0.6e-5
#model_id, bandwidth, threshold = "2982", 0.045, 0.3e-5
#model_id, bandwidth, threshold = "2586", 0.05, 0.6e-5
#model_id, bandwidth, threshold = "8184", 0.05, 0.4e-5
#model_id, bandwidth, threshold = "9000", 0.035, 0.16e-5

# create data used for inferece
print("creating data for model ID {:d}".format(model_id))
mesh_filename = os.path.join(input_folder, '{:d}_remesh.obj'.format(model_id))
data, vox, surface_geodesic = create_single_data(mesh_filename)
print("creating data for model ID {:s}".format(model_id))
mesh_filename = os.path.join(input_folder, '{:s}_remesh.obj'.format(model_id))
data, vox, surface_geodesic, translation_normalize, scale_normalize = create_single_data(mesh_filename)
data.to(device)

print("predicting joints")
data = predict_joints(data, vox, jointNet, threshold, bandwidth=bandwidth, mesh_filename=mesh_filename)
data = predict_joints(data, vox, jointNet, threshold, bandwidth=bandwidth,
mesh_filename=mesh_filename.replace("_remesh.obj", "_normalized.obj"))
data.to(device)
print("predicting connectivity")
pred_skeleton = predict_skeleton(data, vox, rootNet, boneNet, mesh_filename=mesh_filename)
pred_skeleton = predict_skeleton(data, vox, rootNet, boneNet,
mesh_filename=mesh_filename.replace("_remesh.obj", "_normalized.obj"))
print("predicting skinning")
pred_rig = predict_skinning(data, pred_skeleton, skinNet, surface_geodesic, mesh_filename)
pred_rig = predict_skinning(data, pred_skeleton, skinNet, surface_geodesic,
mesh_filename.replace("_remesh.obj", "_normalized.obj"))

# here we reverse the normalization to the original scale and position
pred_rig.normalize(scale_normalize, -translation_normalize)

print("Saving result")
if True:
# here we use original mesh tesselation (without remeshing)
mesh_filename_ori = os.path.join(input_folder, '{:d}_ori.obj'.format(model_id))
mesh_filename_ori = os.path.join(input_folder, '{:s}_ori.obj'.format(model_id))
pred_rig = tranfer_to_ori_mesh(mesh_filename_ori, mesh_filename, pred_rig)
pred_rig.save(mesh_filename_ori.replace('.obj', '_rig.txt'))
else:
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