def run_tailornet():
gender = 'female'
garment_class = 'pant'
thetas, betas, gammas = get_single_frame_inputs(garment_class, gender)
# # uncomment the line below to run inference on sequence data
# thetas, betas, gammas = get_sequence_inputs(garment_class, gender)
# load model
tn_runner = get_tn_runner(gender=gender, garment_class=garment_class)
smpl = SMPL4Garment(gender=gender)
# make out directory if doesn't exist
if not os.path.isdir(OUT_PATH):
os.mkdir(OUT_PATH)
# run inference
for i, (theta, beta, gamma) in enumerate(zip(thetas, betas, gammas)):
print(i, len(thetas))
# normalize y-rotation to make it front facing
theta_normalized = normalize_y_rotation(theta)
with torch.no_grad():
pred_verts_d = tn_runner.forward(
thetas=torch.from_numpy(theta_normalized[None, :].astype(np.float32)).cuda(),
betas=torch.from_numpy(beta[None, :].astype(np.float32)).cuda(),
gammas=torch.from_numpy(gamma[None, :].astype(np.float32)).cuda(),
)[0].cpu().numpy()
# get garment from predicted displacements
body, pred_gar = smpl.run(beta=beta, theta=theta, garment_class=garment_class, garment_d=pred_verts_d)
pred_gar = remove_interpenetration_fast(pred_gar, body)
# save body and predicted garment
body.write_ply(os.path.join(OUT_PATH, "body_{:04d}.ply".format(i)))
pred_gar.write_ply(os.path.join(OUT_PATH, "pred_gar_{:04d}.ply".format(i)))
def run_tailornet():
gender = 'male'
garment_class = 'short-pant'
garment_class_pairs = {
'shirt': [],
't-shirt': [],
'pant': ['shirt', 't-shirt'],
'short-pant': ['shirt', 't-shirt'],
'skirt': ['shirt', 't-shirt']
}
thetas, betas, gammas = get_single_frame_inputs(garment_class, gender)
# # uncomment the line below to run inference on sequence data
# thetas, betas, gammas = get_sequence_inputs(garment_class, gender)
# load model
tn_runner = get_tn_runner(gender=gender, garment_class=garment_class)
# from trainer.base_trainer import get_best_runner
# tn_runner = get_best_runner("/BS/cpatel/work/data/learn_anim/tn_baseline/{}_{}/".format(garment_class, gender))
smpl = SMPL4Garment(gender=gender)
# make out directory if doesn't exist
if not os.path.isdir(OUT_PATH):
os.mkdir(OUT_PATH)
# run inference
for i, (theta, beta, gamma) in enumerate(zip(thetas, betas, gammas)):
print(i, len(thetas))
# normalize y-rotation to make it front facing
theta_normalized = normalize_y_rotation(theta)
with torch.no_grad():
pred_verts_d = tn_runner.forward(
thetas=torch.from_numpy(theta_normalized[None, :].astype(
np.float32)).cuda(),
betas=torch.from_numpy(beta[None, :].astype(
np.float32)).cuda(),
gammas=torch.from_numpy(gamma[None, :].astype(
np.float32)).cuda(),
)[0].cpu().numpy()
# get garment from predicted displacements
body, pred_gar = smpl.run(beta=beta,
theta=theta,
garment_class=garment_class,
garment_d=pred_verts_d)
pred_gar = remove_interpenetration_fast(pred_gar, body)
hv, hf, mapping = get_hres(pred_gar.v, pred_gar.f)
pred_gar_hres = Mesh(hv, hf)
for garment_class_pair in garment_class_pairs[garment_class]:
gar_pair_hres = Mesh(filename=os.path.join(
OUT_PATH, "bcnet_model_{}_gar_hres_{}_{:04d}.obj".format(
BCNET_MODEL_NUM, garment_class_pair, i)))
# print(len(body.v), body.f.min(), body.f.max())
# print(len(pred_gar_hres.v), pred_gar_hres.f.min(), pred_gar_hres.f.max())
# print(np.vstack((body.v, pred_gar_hres.v)).shape, np.min(pred_gar_hres.f + len(body.v)), np.max(pred_gar_hres.f + len(body.v)))
# gar_pair_hres = remove_interpenetration_fast(gar_pair_hres, pred_gar_hres)
for _ in range(3):
gar_pair_hres = remove_interpenetration_fast(
gar_pair_hres,
Mesh(np.vstack((body.v, pred_gar_hres.v)),
np.vstack((body.f, pred_gar_hres.f + len(body.v)))))
gar_pair_hres.write_obj(
os.path.join(
OUT_PATH,
"bcnet_model_{}_gar_hres_{}_inter_{}_{:04d}.obj".format(
BCNET_MODEL_NUM, garment_class_pair, garment_class,
i)))
# save body and predicted garment
body.write_obj(
os.path.join(OUT_PATH,
"bcnet_model_{}.obj".format(BCNET_MODEL_NUM)))
pred_gar.write_obj(
os.path.join(
OUT_PATH, "bcnet_model_{}_gar_{}_{:04d}.obj".format(
BCNET_MODEL_NUM, garment_class, i)))
pred_gar_hres.write_obj(
os.path.join(
OUT_PATH, "bcnet_model_{}_gar_hres_{}_{:04d}.obj".format(
BCNET_MODEL_NUM, garment_class, i)))
