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general {
base_exp_dir = exp/CASE_NAME/wmask
tag = "train_retargeted_shadow_hand_seq_102_mano_sparse_retargeting_"
recording = [
./,
./models
]
}
dataset {
data_dir = public_data/CASE_NAME/
render_cameras_name = cameras_sphere.npz
object_cameras_name = cameras_sphere.npz
obj_idx = 102
}
train {
learning_rate = 5e-4
learning_rate_alpha = 0.05
end_iter = 300000
batch_size = 1024
validate_resolution_level = 4
warm_up_end = 5000
anneal_end = 0
use_white_bkgd = False
# save_freq = 10000
save_freq = 10000
val_freq = 20
val_mesh_freq = 20
report_freq = 10
igr_weight = 0.1
mask_weight = 0.1
}
model {
optimize_dyn_actions = True
optimize_robot = True
use_penalty_based_friction = True
use_split_params = False
use_sqr_spring_stiffness = True
use_pre_proj_frictions = True
use_sqrt_dist = True
contact_maintaining_dist_thres = 0.2
robot_actions_diff_coef = 0.001
use_sdf_as_contact_dist = True
#
use_contact_dist_as_sdf = False
use_glb_proj_delta = True
# penetration_proj_k_to_robot = 30
penetrating_depth_penalty = 1.0
train_states = True
minn_dist_threshold = 0.000
obj_mass = 30.0
use_LBFGS = True
use_LBFGS = False
use_mano_hand_for_test = False # use the dynamic mano model here #
extract_delta_mesh = False
freeze_weights = True
gt_act_xs_def = False
use_bending_network = True
### for ts = 3 ###
# use_delta_bending = False
### for ts = 3 ###
sim_model_path = "rsc/shadow_hand_description/shadowhand_new.urdf"
mano_sim_model_path = "rsc/mano/mano_mean_wcollision_scaled_scaled_0_9507_nroot.urdf"
obj_sdf_fn = "data/grab/102/102_obj.npy"
kinematic_mano_gt_sv_fn = "data/grab/102/102_sv_dict.npy"
scaled_obj_mesh_fn = "data/grab/102/102_obj.obj"
bending_net_type = "active_force_field_v18"
sim_num_steps = 1000000
n_timesteps = 60
optim_sim_model_params_from_mano = False
penetration_determining = "sdf_of_canon"
train_with_forces_to_active = False
loss_scale_coef = 1000.0
use_same_contact_spring_k = False
use_optimizable_params = True #
train_residual_friction = True
mano_mult_const_after_cent = 1.0
optimize_glb_transformations = True
no_friction_constraint = False
optimize_active_object = True
loss_tangential_diff_coef = 0
optimize_with_intermediates = True
using_delta_glb_trans = False
train_multi_seqs = False
use_split_network = True
use_delta_bending = True
##### contact spring model settings ####
minn_dist_threshold_robot_to_obj = 0.1
penetration_proj_k_to_robot_friction = 10000000.0
penetration_proj_k_to_robot = 4000000.0
##### contact spring model settings ####
###### ######
drive_pointset = "states"
fix_obj = True
optimize_rules = False
train_pointset_acts_via_deltas = False
load_optimized_init_actions = ""
load_optimized_init_transformations = ""
ckpt_fn = ""
retar_only_glb = True
# use_multi_stages = True
###### Stage 1: threshold, ks settings 1, optimize offsets ######
use_opt_rigid_translations=True
train_def = True
optimizable_rigid_translations=True
nerf {
D = 8,
d_in = 4,
d_in_view = 3,
W = 256,
multires = 10,
multires_view = 4,
output_ch = 4,
skips=[4],
use_viewdirs=True
}
sdf_network {
d_out = 257,
d_in = 3,
d_hidden = 256,
n_layers = 8,
skip_in = [4],
multires = 6,
bias = 0.5,
scale = 1.0,
geometric_init = True,
weight_norm = True,
}
variance_network {
init_val = 0.3
}
rendering_network {
d_feature = 256,
mode = idr,
d_in = 9,
d_out = 3,
d_hidden = 256,
n_layers = 4,
weight_norm = True,
multires_view = 4,
squeeze_out = True,
}
neus_renderer {
n_samples = 64,
n_importance = 64,
n_outside = 0,
up_sample_steps = 4 ,
perturb = 1.0,
}
bending_network {
multires = 6,
bending_latent_size = 32,
d_in = 3,
rigidity_hidden_dimensions = 64,
rigidity_network_depth = 5,
use_rigidity_network = False,
bending_n_timesteps = 10,
}
}
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