import sys sys.path.append('/mnt/shenzhen2cephfs/capybarali/codes/humanoid') import torch, yaml, os from tqdm import tqdm from smplx import SMPLX from src.utils.rotation_conversions import quaternion_to_matrix, matrix_to_rotation_6d, matrix_to_axis_angle, rotation_6d_to_matrix, matrix_to_quaternion import numpy as np from argparse import ArgumentParser import joblib from data.vis import vis_3d_motion from data.vis_g1 import vis_3d_g1 from copy import deepcopy device = torch.device("cuda" if torch.cuda.is_available() else "cpu") _debug_save_count = 0 _debug_save_dir = 'data/debug_transform' parser = ArgumentParser(description="Launch MoCap processing") parser.add_argument('--save_root', type=str, default="data/final_data") parser.add_argument('--start_idx', type=int, default=0) parser.add_argument('--interval', type=int, default=1) args = parser.parse_args() os.makedirs(args.save_root, exist_ok=True) os.makedirs(os.path.join(args.save_root, 'motions'), exist_ok=True) smplx_model = SMPLX( model_path='checkpoints/human_model/SMPLX_NEUTRAL.npz', use_pca=False, num_expression_coeffs=100, num_betas=10, ext='npz' ).to(device) def extract_g1_component(x): vel_xy = x[:, :2] dof = x[:, -29:] root_rot_mat = rotation_6d_to_matrix(x[:, 2:8]) trans_xyz = torch.cat([torch.cumsum(vel_xy, dim=0), x[:, 10]]) rot_mat = torch.tensor([[1, 0, 0], [0, 0, -1], [0, 1, 0]]).float() global_orient_mat = torch.from_numpy(root_rot_mat).squeeze(1) global_orient_mat = torch.einsum('ij,tjk->tik', rot_mat, global_orient_mat) rot_quat = matrix_to_quaternion(global_orient_mat) # (T, 4) wxyz order transl = torch.from_numpy(trans_xyz).float() transl = torch.einsum('ij,tj->ti', rot_mat, transl) return dof, rot_quat, trans_xyz def get_smplx_motion(data_path): smplx_data = joblib.load(data_path) transl = torch.from_numpy(smplx_data['smplx_transl']) betas = torch.from_numpy(np.load('checkpoints/humanoid_model/g1/betas.npy')) global_orient = torch.from_numpy(smplx_data['smplx_global_orient']) body_pose = torch.from_numpy(smplx_data['smplx_body_pose']) N = transl.shape[0] motion_params = dict( transl=transl, global_orient=global_orient, body_pose=body_pose, betas=betas.unsqueeze(0).repeat(N, 1).float() ) # 1. process positions frame_params = {k: v.to(device) for k, v in motion_params.items()} frame_params['leye_pose'] = torch.zeros((N, 3)).to(device) frame_params['reye_pose'] = torch.zeros((N, 3)).to(device) frame_params['left_hand_pose'] = torch.zeros((N, 45)).to(device) frame_params['right_hand_pose'] = torch.zeros((N, 45)).to(device) frame_params['jaw_pose'] = torch.zeros((N, 3)).to(device) frame_params['expression'] = torch.zeros((N, 100)).to(device) output = smplx_model(**frame_params) position_data = output.joints.detach().cpu()[:, :22] # T, 22 ,3 position_val_data = position_data[1:] - position_data[:-1] root_idx = 0 # put on floor and put root on origin for the first frame ori = deepcopy(position_data[0, root_idx]) # first frame root position y_min = torch.min(position_data[:, :, 1]) ori[1] = y_min position_data = position_data - ori velocities_root = position_data[1:, root_idx, :] - position_data[:-1, root_idx, :] position_data[:,:,0] -= position_data[:,0:1,0] position_data[:,:,2] -= position_data[:,0:1,2] T, njoint, _ = position_data.shape final_x = torch.zeros((T, 2 + 6 + njoint * 3 + njoint * 3)) final_x[1:, 0] = velocities_root[:, 0] final_x[1:, 1] = velocities_root[:, 1] final_x[:, 2:2+6] = matrix_to_rotation_6d(global_orient) final_x[:, 8:8+njoint*3] = position_data.flatten(1, 2) final_x[1:, 8+njoint*3:8+njoint*6] = position_val_data.flatten(1, 2) # T, 140 return final_x def get_g1_motion(data_path): global _debug_save_count rotation_matrix = torch.tensor([[1.0, 0, 0], [0, 0, -1], [0, 1, 0]]).inverse() g1_data = joblib.load(data_path) dof = g1_data['g1_dof'] # T, 29 global_orient = g1_data['g1_root_ori'] # T, 4, wxyz joints = g1_data['g1_joints'] # T, 30, 3 # save before-transform data for first 3 motions if _debug_save_count < 3: os.makedirs(_debug_save_dir, exist_ok=True) np.savez( os.path.join(_debug_save_dir, f'motion_{_debug_save_count:02d}_before.npz'), g1_trans=joints, g1_root_rot=global_orient, g1_dof=dof, ) global_orient_mat = quaternion_to_matrix(torch.from_numpy(global_orient)).float() global_orient_mat = torch.einsum('ij,tjk->tik', rotation_matrix, global_orient_mat) global_orient = matrix_to_axis_angle(global_orient_mat) position_data = torch.einsum('ij,tkj->tki', rotation_matrix, torch.from_numpy(joints).float()) position_val_data = position_data[1:] - position_data[:-1] # save after-transform data for first 3 motions if _debug_save_count < 3: np.savez( os.path.join(_debug_save_dir, f'motion_{_debug_save_count:02d}_after.npz'), g1_trans=position_data, g1_root_rot=global_orient.numpy(), g1_dof=dof, ) root_idx = 0 # put on floor and put root on origin for the first frame ori = deepcopy(position_data[0, root_idx]) # first frame root position y_min = torch.min(position_data[:, :, 1]) ori[1] = y_min position_data = position_data - ori velocities_root = position_data[1:, root_idx, :] - position_data[:-1, root_idx, :] position_data[:,:,0] -= position_data[:,0:1,0] position_data[:,:,2] -= position_data[:,0:1,2] T, njoint, _ = position_data.shape final_x = torch.zeros((T, 2 + 6 + njoint * 3 + njoint * 3)) final_x[1:, 0] = velocities_root[:, 0] final_x[1:, 1] = velocities_root[:, 1] final_x[:, 2:2+6] = matrix_to_rotation_6d(global_orient) final_x[:, 8:8+njoint*3] = position_data.flatten(1, 2) final_x[1:, 8+njoint*3:8+njoint*6] = position_val_data.flatten(1, 2) # T, 140 final_x = torch.concat([final_x, torch.from_numpy(dof)], dim=-1) return final_x # 217 # python -m data.motionmillion.tools.process --save_root "data/motionmillion/final_data" if __name__ == '__main__': with open('data/merge_gmr_retarget_smplx_path.txt', 'r') as f: paths = f.readlines() for line in tqdm(paths[args.start_idx::args.interval]): data_path = line.strip() smplx_motion = get_smplx_motion(data_path) g1_motion = get_g1_motion(data_path) if smplx_motion.shape[0] != g1_motion.shape[0]: min_len = min(smplx_motion.shape[0], g1_motion.shape[0]) smplx_motion = smplx_motion[:min_len] g1_motion = g1_motion[:min_len] data = dict( g1_motion=g1_motion, smplx_motion=smplx_motion, ) data_path = data_path.replace('.npz', '.pkl') save_path = args.save_root + '/motions/' + '/'.join(data_path.split('/')[2:]) # os.makedirs(os.path.dirname(save_path), exist_ok=True) # joblib.dump(data, save_path)