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import torch
import numpy as np
import os
from ..mesh.io import save_obj, to_mesh
from ..mesh.smpl2mesh import SMPL2Mesh
from ..skeleton import SkeletonAMASS, convert2humanml
from ..skeleton2smpl.skeleton2smpl import Skeleton2Obj
import json
from scipy.ndimage import gaussian_filter1d
def save_mesh(vertices, faces, output_dir, output_idx=None):
"""Save mesh vertices and faces to obj files.
Args:
vertices: Mesh vertices with shape (num_vertices, 3, num_frames)
faces: Mesh faces
output_dir: Directory to save the obj files
output_idx: Index of the prediction to save
"""
# create obs_obj and pred_{i}_obj folders
obs_obj_dir = os.path.join(output_dir, "obs_obj")
os.makedirs(obs_obj_dir, exist_ok=True)
if output_idx is not None:
pred_obj_dir = os.path.join(output_dir, f"pred_{output_idx}_obj")
os.makedirs(pred_obj_dir, exist_ok=True)
# Ensure vertices has the correct shape (num_vertices, 3, num_frames)
if len(vertices.shape) == 4: # If we have an extra batch dimension
vertices = vertices.squeeze(0) # Remove the batch dimension
# Convert vertices to numpy if it's a tensor
if torch.is_tensor(vertices):
vertices = vertices.cpu().numpy()
if torch.is_tensor(faces):
faces = faces.cpu().numpy()
print(f"Processing mesh for output index {output_idx}")
# Save first 30 frames to obs_obj directory
for frame_i in range(30):
file_path = os.path.join(output_dir, f"obs_obj/frame{frame_i:03d}.obj")
frame_vertices = vertices[:, :, frame_i]
mesh = to_mesh(frame_vertices, faces)
save_obj(mesh, file_path)
# Save remaining frames to pred directory
if output_idx is not None:
for frame_i in range(30, vertices.shape[2]):
file_path = os.path.join(output_dir, f"pred_{output_idx}_obj/frame{frame_i:03d}.obj")
frame_vertices = vertices[:, :, frame_i]
mesh = to_mesh(frame_vertices, faces)
save_obj(mesh, file_path)
print(f"Saved obj files to [{output_dir}]")
def main():
num_smplify_iters = 20 # This is what requires most time. It can be decreased or increasd depending on the output quality we want (or how quick we canr each it)
device = "cuda"
# get observation smpl params
json_file_path = "./smpl_params.json"
with open(json_file_path, "r") as json_file:
loaded_data = json.load(json_file)
person_idx = 0
smpl_dict_last_obs = loaded_data[-1]
smpl_dict_last_obs = {k: torch.from_numpy(np.array(v)).float().to(device) for k,v in smpl_dict_last_obs.items()}
# select just some of the motions
# TO DO use the previous code with the limb length variance error to choose the sample
# Or pick the most diverse
skeleton = SkeletonAMASS
pred_files = ['pred_closest_GT.npy']
pred_motions = torch.from_numpy(np.load(pred_files[0], allow_pickle=True)).to(device)
pred_motions = pred_motions.view(-1, 22, 3)
pred_motions = convert2humanml(pred_motions, skeleton.LANDMARKS, skeleton.TO_HUMANML_NAMES)
loaded_data = np.load("obs_data.npz", allow_pickle=True)
rot_motions_obs = loaded_data["rot_motions_obs"]
smpl_dict_obs = loaded_data['smpl_dict_obs'].item()
smpl_dict_obs = {k: torch.from_numpy(v).to(device) for k,v in smpl_dict_obs.items()}
# init_params = {}
init_params = {}
current_motions = pred_motions
init_params["betas"] = smpl_dict_last_obs["betas"][person_idx].unsqueeze(0).expand(current_motions.shape[0], -1)
init_params["pose"] = smpl_dict_last_obs["body_pose"][person_idx].view(-1, 3)
# init_params["pose"] = torch.stack([init_params["pose"][..., 0], init_params["pose"][..., 2], -init_params["pose"][..., 1]], dim=-1)
assert init_params["pose"].shape[0] == 24, "the body pose should have 24 joints, it is the output of NLF"
init_params["pose"] = init_params["pose"].unsqueeze(0).expand(current_motions.shape[0], -1, -1).view(current_motions.shape[0], -1).to(device)
# init_params["pose"] = (torch.tensor([ 0.4531, 0.3044, 0.2968, -0.2239, 0.0174, 0.0925, -0.2378, -0.0465,
# -0.0786, 0.2782, 0.0141, 0.0138, 0.4328, -0.0629, -0.0961, 0.5043,
# 0.0035, 0.0610, 0.0230, -0.0317, 0.0058, 0.0070, 0.1317, -0.0544,
# -0.0589, -0.1752, 0.1355, 0.0134, -0.0037, 0.0089, -0.2093, 0.1600,
# 0.1092, -0.0387, 0.0824, -0.2041, -0.0056, -0.0075, -0.0035, -0.0237,
# -0.1248, -0.2736, -0.0459, 0.1991, 0.2373, 0.0667, -0.0405, 0.0329,
# 0.0536, -0.2914, -0.6969, 0.0559, 0.2858, 0.6525, 0.1222, -0.9116,
# 0.2383, -0.0366, 0.9237, -0.2554, -0.0657, -0.1045, 0.0501, -0.0388,
# 0.0909, -0.0707, -0.1437, -0.0590, -0.1801, -0.0875, 0.1093, 0.2009])
# .unsqueeze(0)
# .repeat(pred_motions.shape[0], 1)
# .float().to(device)
# )
# init_params["pose"] = (torch.tensor([ 0.4531, 0.3044, -1, -0.2239, 0.0174, 0.0925, -0.2378, -0.0465,
# -0.0786, 0.2782, 0.0141, 0.0138, 0.4328, -0.0629, -0.0961, 0.5043,
# 0.0035, 0.0610, 0.0230, -0.0317, 0.0058, 0.0070, 0.1317, -0.0544,
# -0.0589, -0.1752, 0.1355, 0.0134, -0.0037, 0.0089, -0.2093, 0.1600,
# 0.1092, -0.0387, 0.0824, -0.2041, -0.0056, -0.0075, -0.0035, -0.0237,
# -0.1248, -0.2736, -0.0459, 0.1991, 0.2373, 0.0667, -0.0405, 0.0329,
# 0.0536, -0.2914, -0.6969, 0.0559, 0.2858, 0.6525, 0.1222, -0.9116,
# 0.2383, -0.0366, 0.9237, -0.2554, -0.0657, -0.1045, 0.0501, -0.0388,
# 0.0909, -0.0707, -0.1437, -0.0590, -0.1801, -0.0875, 0.1093, 0.2009])
# .unsqueeze(0)
# .repeat(pred_motions.shape[0], 1)
# .float().to(device)
# )
init_params = {}
init_params["betas"] = smpl_dict_obs["betas"][-1].unsqueeze(0).expand(pred_motions.shape[0], -1).to(device)
# init_params["betas"] = torch.tensor([10.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]).repeat(pred_motions.shape[0], 1).to(device)
init_params["pose"] = smpl_dict_obs["pose"][-1].unsqueeze(0).expand(pred_motions.shape[0], -1, -1).view(pred_motions.shape[0], -1).to(device)
init_params["cam"] = smpl_dict_obs["cam"][-1].unsqueeze(0).expand(pred_motions.shape[0], -1, -1).to(device)
init_params["pose"] = torch.zeros(pred_motions.shape[0], 72).to(device)
# # smpl_dict_last_obs["transl"][person_idx] = torch.stack([-smpl_dict_last_obs["transl"][person_idx][..., 2], smpl_dict_last_obs["transl"][person_idx][..., 0], -smpl_dict_last_obs["transl"][person_idx][..., 1]], dim=-1)
# init_params["cam"] = smpl_dict_last_obs["transl"][person_idx].unsqueeze(0).unsqueeze(-2).expand(current_motions.shape[0], -1, -1).to(device)
skeleton2obj = Skeleton2Obj(
device=device, num_smplify_iters=num_smplify_iters,
smpl_model_dir="./models/body_models/", #path to smpl body models
gmm_model_dir="./models/joint2smpl_models/", #path to gmm model
)
rot_motions, smpl_dict = skeleton2obj.convert_motion_2smpl(pred_motions, hmp=True, init_params=init_params, fix_betas=True)
smpl2mesh = SMPL2Mesh(device)
vertices, faces = smpl2mesh.convert_smpl_to_mesh(rot_motions, pred_motions)
pred_files = [('./hanyu_t')]
vertices = vertices.reshape(*vertices.shape[:2], len(pred_files), -1)
for v, npy_file in zip(np.moveaxis(vertices, 2, 0), pred_files):
save_mesh(v, faces, npy_file)
def process_motion(smpl_params_path, pred_motions_path, device, sorted_idx, output_dir='./hanyu_obj'):
num_smplify_iters = 40
device = "cuda"
# Load and preprocess data once
with open(smpl_params_path, "r") as json_file:
loaded_data = json.load(json_file)
person_idx = 0
smpl_dict_last_obs = {k: torch.from_numpy(np.array(v)).float().to(device) for k,v in loaded_data[1].items()}
# Load predictions and preprocess
pred_motions = torch.from_numpy(np.load(pred_motions_path, allow_pickle=True)).to(device)
pred_motions = pred_motions.squeeze(0)[sorted_idx] # Shape: (num_samples, num_frames, 22, 3)
# Initialize skeleton once
skeleton = SkeletonAMASS
# Pre-allocate lists for results
all_vertices = []
all_faces = None
first_rot_motions = None
first_smpl_dict = None
# Pre-compute common parameters
init_betas = smpl_dict_last_obs["betas"][person_idx]
init_pose = smpl_dict_last_obs["body_pose"][person_idx].view(-1, 3)
assert init_pose.shape[0] == 24, "the body pose should have 24 joints, it is the output of NLF"
# Initialize SMPL2Mesh once
smpl2mesh = SMPL2Mesh(device)
# Initialize Skeleton2Obj once
skeleton2obj = Skeleton2Obj(
device=device,
num_smplify_iters=num_smplify_iters,
smpl_model_dir="./models/body_models/",
gmm_model_dir="./models/joint2smpl_models/",
)
# Process each sample
for sample_idx in range(len(sorted_idx)):
current_motions = pred_motions[sample_idx].reshape(-1, 22, 3)
current_motions = convert2humanml(current_motions, skeleton.LANDMARKS, skeleton.TO_HUMANML_NAMES)
# Prepare init params efficiently
init_params = {
"betas": init_betas.unsqueeze(0).expand(current_motions.shape[0], -1),
"pose": init_pose.unsqueeze(0).expand(current_motions.shape[0], -1, -1).view(current_motions.shape[0], -1).to(device),
"cam": torch.Tensor([0.0, 0.0, 0.0]).unsqueeze(0).to(device)
}
# Get first frame parameters if not already computed
if first_rot_motions is None:
with torch.set_grad_enabled(True):
first_rot_motions, first_smpl_dict = skeleton2obj.convert_motion_2smpl(
current_motions[0].unsqueeze(0).to(device),
hmp=False,
init_params=None,
fix_betas=False
)
# Update init params with first frame data
init_params["pose"] = first_rot_motions.repeat(current_motions.shape[0], 1)
init_params["betas"] = first_smpl_dict["betas"].repeat(current_motions.shape[0], 1)
init_params["cam"] = first_smpl_dict["cam"]
# Convert motion to SMPL
with torch.set_grad_enabled(True):
rot_motions, smpl_dict = skeleton2obj.convert_motion_2smpl(
current_motions,
hmp=True,
init_params=init_params,
fix_betas=True
)
# Process rotations
rot_motions = rot_motions.reshape(-1, 24, 3).reshape(-1, 72)
# Apply Gaussian smoothing to specific parameters
rot_motions_np = rot_motions.cpu().numpy()
sigma = 3.0
for param_idx in range(36, 40):
rot_motions_np[:, param_idx] = gaussian_filter1d(rot_motions_np[:, param_idx], sigma=sigma)
for param_idx in range(45, 49):
rot_motions_np[:, param_idx] = gaussian_filter1d(rot_motions_np[:, param_idx], sigma=sigma)
rot_motions = torch.from_numpy(rot_motions_np).to(device)
# Convert to mesh
vertices, faces = smpl2mesh.convert_smpl_to_mesh(rot_motions, current_motions)
if all_faces is None:
all_faces = faces
all_vertices.append(vertices)
# Convert faces to numpy once at the end
if torch.is_tensor(all_faces):
all_faces = all_faces.cpu().numpy()
# Save results
os.makedirs(output_dir, exist_ok=True)
for i, vertices in enumerate(all_vertices):
print(vertices.shape)
save_mesh(vertices, all_faces, output_dir, output_idx=i)
def smpl_to_mesh(smpl_params_path, pred_motions_path, device, sorted_idx):
num_smplify_iters = 20 # This is what requires most time. It can be decreased or increasd depending on the output quality we want (or how quick we canr each it)
device = "cuda"
# Process each sample separately to maintain frame order
all_vertices = []
all_faces = None
# get observation smpl params
json_file_path = smpl_params_path
with open(json_file_path, "r") as json_file:
loaded_data = json.load(json_file)
person_idx = 0
smpl_dict_last_obs = loaded_data[1]
smpl_dict_last_obs = {k: torch.from_numpy(np.array(v)).float().to(device) for k,v in smpl_dict_last_obs.items()}
pred_motions = torch.from_numpy(np.load(pred_motions_path, allow_pickle=True)).to(device)
current_motions = pred_motions[sorted_idx][:30]
smpl2mesh = SMPL2Mesh(device)
init_params = {}
init_params["betas"] = smpl_dict_last_obs["betas"][person_idx].unsqueeze(0).expand(current_motions.shape[0], -1)
init_params["pose"] = smpl_dict_last_obs["body_pose"][person_idx].view(-1, 3)
# init_params["pose"] = torch.stack([init_params["pose"][..., 0], init_params["pose"][..., 2], -init_params["pose"][..., 1]], dim=-1)
assert init_params["pose"].shape[0] == 24, "the body pose should have 24 joints, it is the output of NLF"
init_params["pose"] = init_params["pose"].unsqueeze(0).expand(current_motions.shape[0], -1, -1).view(current_motions.shape[0], -1).to(device)
# rot_motions is a tensor 30,72
rot_motions = torch.zeros(30, 72).to(device)
current_motions = current_motions[0, 0, :30, :, :]
# rot_motions[frame_id] = loaded_data[frame_id]["pose"]
for frame_id in range(30):
# print(f"Processing frame {frame_id}")
# rot_motions.append(loaded_data[frame_id]["body_pose"])
temp = torch.tensor(loaded_data[frame_id]["body_pose"]).to(device).reshape(-1, 24, 3)
# temp = torch.stack([temp[..., 0], temp[..., 2], -temp[..., 1]], dim=-1)
rot_motions[frame_id] = temp.view(-1, 72)
rot_motions[frame_id, 0] = 1.6005e+00 # X-axis rotation
rot_motions[frame_id, 1] = 8.2655e-01 # Y-axis rotation
rot_motions[frame_id, 2] = 9.4306e-01 # Z-axis rotation
# rot_motions = torch.tensor([[ 1.6005e+00, 8.2655e-01, 9.4306e-01, -1.0372e-01, 2.7821e-02,
# -3.8120e-01, -6.2227e-01, -1.3174e-01, -3.9679e-01, -5.5898e-02,
# 4.0376e-02, 1.0782e-01, -2.1775e-02, 9.2568e-02, -8.4396e-02,
# 9.6487e-01, 4.5018e-02, 2.5192e-01, -1.0397e-01, 1.1797e-01,
# -1.3300e-01, -1.0074e+00, 5.0848e-01, 5.7921e-01, -6.9055e-01,
# -4.2592e-02, 2.1878e-01, 1.1854e-01, 1.6012e-01, -7.8607e-02,
# 3.0257e-01, 3.8598e-02, -5.1632e-01, 1.3716e-01, 1.6491e-01,
# 7.4003e-02, 2.7080e-01, 2.8428e-01, -1.6114e-01, -2.1017e-01,
# 3.2442e-01, 5.4414e-01, 1.6220e-01, -2.2343e-02, -5.5549e-01,
# -5.5686e-01, 1.4427e-01, 5.9082e-02, -8.0001e-02, 2.5315e-01,
# -4.6155e-01, -2.7584e-01, 2.5945e-03, 4.7265e-01, -5.5788e-02,
# -5.2776e-01, 8.8441e-02, 1.2666e-01, 1.8338e-01, 2.2411e-02,
# 2.5319e-02, -2.4700e-01, -4.3147e-01, -9.9603e-02, 2.8157e-01,
# 2.2922e-01, -4.2805e-01, 6.8633e-02, 1.2775e-01, -1.7168e-01,
# -6.4354e-02, -4.8196e-02],
# [ 1.6031e+00, 8.6938e-01, 9.5093e-01, -2.4728e-01, -2.5365e-02,
# -3.5090e-01, -6.2714e-01, -1.3281e-01, -3.8726e-01, -7.5513e-02,
# 5.1388e-02, 1.0953e-01, 2.4019e-01, 9.5262e-02, -1.1295e-01,
# 9.5034e-01, 3.7820e-02, 2.5818e-01, -1.1904e-01, 1.0212e-01,
# -1.2446e-01, -1.0170e+00, 5.0628e-01, 5.7588e-01, -6.8644e-01,
# -5.0001e-02, 1.9725e-01, 1.1670e-01, 1.5288e-01, -7.3347e-02,
# 2.7852e-01, 3.9223e-02, -5.1571e-01, 1.3149e-01, 1.5630e-01,
# 7.1519e-02, 2.7341e-01, 2.7110e-01, -1.6775e-01, -2.0063e-01,
# 3.1425e-01, 5.2800e-01, 1.5228e-01, -3.0518e-02, -5.5076e-01,
# -5.4393e-01, 1.5199e-01, 7.1569e-02, -8.7057e-02, 2.5214e-01,
# -4.4306e-01, -2.8668e-01, -2.0587e-02, 4.6127e-01, -4.5369e-02,
# -5.3020e-01, 8.1563e-02, 1.2601e-01, 2.0114e-01, 2.6854e-02,
# 2.3951e-02, -2.4098e-01, -4.2893e-01, -1.0319e-01, 2.7675e-01,
# 2.2407e-01, -4.0467e-01, 7.5838e-02, 1.2791e-01, -1.5939e-01,
# -6.1213e-02, -2.8723e-02],
# [ 1.6110e+00, 8.7997e-01, 9.4755e-01, -2.9480e-01, -5.0045e-02,
# -3.4665e-01, -6.4217e-01, -1.2302e-01, -3.8091e-01, -8.1906e-02,
# 5.4623e-02, 1.0953e-01, 3.0538e-01, 9.1858e-02, -1.1804e-01,
# 9.5882e-01, 4.4244e-02, 2.5869e-01, -1.2163e-01, 9.9909e-02,
# -1.2311e-01, -1.0218e+00, 4.9697e-01, 5.8416e-01, -6.8940e-01,
# -3.8903e-02, 1.9803e-01, 1.0894e-01, 1.5256e-01, -7.2756e-02,
# 2.7297e-01, 4.9584e-02, -5.2945e-01, 1.3066e-01, 1.4965e-01,
# 6.4956e-02, 2.8823e-01, 2.7311e-01, -1.7025e-01, -2.0369e-01,
# 3.1524e-01, 5.3636e-01, 1.5043e-01, -3.4381e-02, -5.6039e-01,
# -5.4034e-01, 1.5133e-01, 7.7262e-02, -8.2864e-02, 2.4608e-01,
# -4.5811e-01, -2.8397e-01, -1.5466e-02, 4.8184e-01, -3.5552e-02,
# -5.3320e-01, 9.3335e-02, 1.2752e-01, 2.0015e-01, 9.5036e-03,
# 2.3358e-02, -2.4074e-01, -4.3686e-01, -1.1153e-01, 2.7872e-01,
# 2.2347e-01, -4.0031e-01, 8.1883e-02, 1.3366e-01, -1.5507e-01,
# -6.3415e-02, -2.5810e-02],
# [ 1.6078e+00, 8.2731e-01, 9.4191e-01, -1.2817e-01, 1.0199e-02,
# -3.8164e-01, -6.3457e-01, -1.2142e-01, -3.9256e-01, -5.9317e-02,
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# -5.0477e-01, 3.9526e-02, 1.1015e-01, 1.3875e-01, 5.6716e-02,
# 2.8294e-02, -2.3300e-01, -4.3126e-01, -8.1621e-02, 2.6405e-01,
# 2.2596e-01, -4.2528e-01, 5.6517e-02, 1.1539e-01, -1.7770e-01,
# -5.2139e-02, -3.9383e-02],
# [ 1.5882e+00, 7.9877e-01, 9.4328e-01, -2.8720e-02, 6.2287e-02,
# -4.0260e-01, -6.0959e-01, -1.4748e-01, -3.9953e-01, -3.8967e-02,
# 3.5521e-02, 1.1342e-01, -1.2244e-01, 1.0107e-01, -7.0909e-02,
# 9.6160e-01, 2.1357e-02, 2.4391e-01, -1.0138e-01, 1.2151e-01,
# -1.3651e-01, -9.7141e-01, 5.3303e-01, 5.5471e-01, -6.6983e-01,
# -8.8017e-02, 2.1613e-01, 1.2814e-01, 1.6456e-01, -8.0381e-02,
# 2.9939e-01, 1.2009e-02, -4.7984e-01, 1.3194e-01, 1.8968e-01,
# 6.8013e-02, 2.3684e-01, 2.5566e-01, -1.6587e-01, -1.9949e-01,
# 3.1261e-01, 5.2987e-01, 1.6465e-01, -2.3355e-03, -5.4320e-01,
# -5.5236e-01, 1.4478e-01, 5.5450e-02, -7.6929e-02, 2.4452e-01,
# -4.5820e-01, -2.8957e-01, 1.8180e-02, 4.3747e-01, -4.2283e-02,
# -5.0567e-01, 4.5314e-02, 1.0879e-01, 1.2915e-01, 5.2620e-02,
# 2.7722e-02, -2.3599e-01, -4.3225e-01, -7.7019e-02, 2.6511e-01,
# 2.2908e-01, -4.3480e-01, 5.2430e-02, 1.1357e-01, -1.8510e-01,
# -5.3361e-02, -4.7402e-02],
# [ 1.5888e+00, 8.1703e-01, 9.5593e-01, -8.2536e-02, 4.7360e-02,
# -3.9056e-01, -6.0633e-01, -1.5541e-01, -4.0785e-01, -4.3322e-02,
# 4.0495e-02, 1.0820e-01, -4.6933e-03, 1.0861e-01, -9.3379e-02,
# 9.6168e-01, 9.5170e-03, 2.4247e-01, -9.9628e-02, 1.1271e-01,
# -1.3622e-01, -9.8448e-01, 5.3791e-01, 5.5485e-01, -6.5830e-01,
# -1.0166e-01, 2.1111e-01, 1.2765e-01, 1.6020e-01, -8.0440e-02,
# 2.9217e-01, 6.0119e-03, -4.7595e-01, 1.2242e-01, 1.9002e-01,
# 5.7588e-02, 2.3393e-01, 2.4151e-01, -1.7059e-01, -1.8763e-01,
# 3.0400e-01, 5.1543e-01, 1.6263e-01, -5.2092e-03, -5.3681e-01,
# -5.4848e-01, 1.4767e-01, 6.1067e-02, -8.5293e-02, 2.4911e-01,
# -4.4561e-01, -2.9968e-01, -1.0991e-03, 4.3592e-01, -4.0780e-02,
# -5.1197e-01, 5.0462e-02, 1.0212e-01, 1.5047e-01, 4.5822e-02,
# 3.3164e-02, -2.3132e-01, -4.2680e-01, -7.8209e-02, 2.6201e-01,
# 2.2518e-01, -4.2874e-01, 5.3289e-02, 1.1230e-01, -1.8014e-01,
# -4.9768e-02, -3.7934e-02]], device='cuda:0')
vertices, faces = smpl2mesh.convert_smpl_to_mesh(rot_motions, current_motions)
if all_faces is None:
all_faces = faces
all_vertices.append(vertices)
if torch.is_tensor(all_faces):
all_faces = all_faces.cpu().numpy()
# Save each sample to its own directory
base_output_dir = './hanyu'
for i, vertices in enumerate(all_vertices):
save_mesh(vertices, all_faces, base_output_dir, output_idx=i) # Use sequential index i instead of sorted_idx[i]
if __name__ == "__main__":
# process_motion("./smpl_params.json", "./predictions/joints3d.npy", "cuda", [25,17,33,7,24])
# main()
# smpl_to_mesh("./smpl_params.json", "./predictions/joints3d.npy", "cuda", [0])
process_motion("./smpl_params.json", "./predictions/joints3d.npy", "cuda", [17]) |