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| import numpy as np | |
| import torch | |
| def npy2info(motions, num_shapes=10): | |
| if isinstance(motions, str): | |
| motions = np.load(motions) | |
| trans = None | |
| gnum = 2 | |
| if isinstance(motions, np.ndarray): | |
| betas = np.zeros([motions.shape[0], num_shapes]).astype(motions.dtype) | |
| else: | |
| betas = torch.zeros([motions.shape[0], num_shapes], dtype=motions.dtype) | |
| if len(motions.shape) == 3: | |
| motions = motions.reshape(motions.shape[0], -1) | |
| if motions.shape[1] in [73, 157, 166]: | |
| gnum = motions[:, -1:][0] | |
| motions = motions[:, :-1] | |
| elif motions.shape[1] in [75, 159, 168]: | |
| gnum = 2 | |
| trans = motions[:, -3::] | |
| motions = motions[:, :-3] | |
| elif motions.shape[1] in [76, 160, 169]: | |
| gnum = motions[:, -1:][0] | |
| trans = motions[:, -4:-1:] | |
| motions = motions[:, :-4] | |
| elif motions.shape[1] in [72 + num_shapes, 156 + num_shapes, 165 + num_shapes]: | |
| betas = motions[:, -num_shapes::] | |
| gnum = 2 | |
| motions = motions[:, :-num_shapes] | |
| elif motions.shape[1] in [73 + num_shapes, 157 + num_shapes, 166 + num_shapes]: | |
| betas = motions[:, -num_shapes::] | |
| gnum = motions[:, -num_shapes-1:-num_shapes:][0] | |
| motions = motions[:, :-num_shapes-1] | |
| elif motions.shape[1] in [75 + num_shapes, 159 + num_shapes, 168 + num_shapes]: | |
| betas = motions[:, -num_shapes::] | |
| gnum = 2 | |
| trans = motions[:, -num_shapes-3:-num_shapes:] | |
| motions = motions[:, :-num_shapes-3] | |
| elif motions.shape[1] in [76 + num_shapes, 160 + num_shapes, 169 + num_shapes]: | |
| betas = motions[:, -num_shapes::] | |
| gnum = motions[:, -num_shapes-1:-num_shapes:][0] | |
| trans = motions[:, -num_shapes-4:-num_shapes-1:] | |
| motions = motions[:, :-num_shapes-4] | |
| if gnum == 0: | |
| gender = "female" | |
| elif gnum == 1: | |
| gender = "male" | |
| else: | |
| gender = "neutral" | |
| return motions, trans, gender, betas | |
| def info2dict(pose, trans=None, betas=None, mode="smpl", device="cuda", index=-1): | |
| if isinstance(pose, np.ndarray): | |
| pose = torch.from_numpy(pose) | |
| if trans is not None and isinstance(trans, np.ndarray): | |
| trans = torch.from_numpy(trans) | |
| if betas is not None and isinstance(betas, np.ndarray): | |
| betas = torch.from_numpy(betas) | |
| elif betas is None: | |
| betas = torch.zeros([pose.shape[0], 10]) | |
| if index != -1: | |
| pose = pose[index:index+1] | |
| if trans is not None: | |
| trans = trans[index:index+1] | |
| betas = betas[index:index+1] | |
| if mode == "smplx": | |
| inputs = { | |
| "global_orient": pose[:, :3].float().to(device), | |
| "body_pose": pose[:, 3:66].float().to(device), | |
| "jaw_pose": pose[:, 66:69].float().to(device), | |
| "leye_pose": pose[:, 69:72].float().to(device), | |
| "reye_pose": pose[:, 72:75].float().to(device), | |
| "left_hand_pose":pose[:, 75:120].float().to(device), | |
| "right_hand_pose":pose[:, 120:].float().to(device), | |
| } | |
| elif mode == "smplh": | |
| inputs = { | |
| "global_orient": pose[:, :3].float().to(device), | |
| "body_pose": pose[:, 3:66].float().to(device), | |
| "left_hand_pose":pose[:, 66:111].float().to(device), | |
| "right_hand_pose":pose[:, 111:].float().to(device), | |
| } | |
| elif mode == "smpl": | |
| inputs = { | |
| "global_orient": pose[:, :3].float().to(device), | |
| "body_pose": pose[:, 3:].float().to(device), | |
| } | |
| if trans is not None: | |
| inputs["transl"] = trans[:, :].float().to(device) | |
| else: | |
| print("No Translation Information") | |
| inputs["betas"] = betas[:, :].float().to(device) | |
| return inputs |