| from scipy.spatial import ConvexHull
|
| import torch
|
| import torch.nn.functional as F
|
| import numpy as np
|
| from tqdm import tqdm
|
|
|
| def normalize_kp(kp_source, kp_driving, kp_driving_initial, adapt_movement_scale=False,
|
| use_relative_movement=False, use_relative_jacobian=False):
|
| if adapt_movement_scale:
|
| source_area = ConvexHull(kp_source['value'][0].data.cpu().numpy()).volume
|
| driving_area = ConvexHull(kp_driving_initial['value'][0].data.cpu().numpy()).volume
|
| adapt_movement_scale = np.sqrt(source_area) / np.sqrt(driving_area)
|
| else:
|
| adapt_movement_scale = 1
|
|
|
| kp_new = {k: v for k, v in kp_driving.items()}
|
|
|
| if use_relative_movement:
|
| kp_value_diff = (kp_driving['value'] - kp_driving_initial['value'])
|
| kp_value_diff *= adapt_movement_scale
|
| kp_new['value'] = kp_value_diff + kp_source['value']
|
|
|
| if use_relative_jacobian:
|
| jacobian_diff = torch.matmul(kp_driving['jacobian'], torch.inverse(kp_driving_initial['jacobian']))
|
| kp_new['jacobian'] = torch.matmul(jacobian_diff, kp_source['jacobian'])
|
|
|
| return kp_new
|
|
|
| def headpose_pred_to_degree(pred):
|
| device = pred.device
|
| idx_tensor = [idx for idx in range(66)]
|
| idx_tensor = torch.FloatTensor(idx_tensor).type_as(pred).to(device)
|
| pred = F.softmax(pred)
|
| degree = torch.sum(pred*idx_tensor, 1) * 3 - 99
|
| return degree
|
|
|
| def get_rotation_matrix(yaw, pitch, roll):
|
| yaw = yaw / 180 * 3.14
|
| pitch = pitch / 180 * 3.14
|
| roll = roll / 180 * 3.14
|
|
|
| roll = roll.unsqueeze(1)
|
| pitch = pitch.unsqueeze(1)
|
| yaw = yaw.unsqueeze(1)
|
|
|
| pitch_mat = torch.cat([torch.ones_like(pitch), torch.zeros_like(pitch), torch.zeros_like(pitch),
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| torch.zeros_like(pitch), torch.cos(pitch), -torch.sin(pitch),
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| torch.zeros_like(pitch), torch.sin(pitch), torch.cos(pitch)], dim=1)
|
| pitch_mat = pitch_mat.view(pitch_mat.shape[0], 3, 3)
|
|
|
| yaw_mat = torch.cat([torch.cos(yaw), torch.zeros_like(yaw), torch.sin(yaw),
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| torch.zeros_like(yaw), torch.ones_like(yaw), torch.zeros_like(yaw),
|
| -torch.sin(yaw), torch.zeros_like(yaw), torch.cos(yaw)], dim=1)
|
| yaw_mat = yaw_mat.view(yaw_mat.shape[0], 3, 3)
|
|
|
| roll_mat = torch.cat([torch.cos(roll), -torch.sin(roll), torch.zeros_like(roll),
|
| torch.sin(roll), torch.cos(roll), torch.zeros_like(roll),
|
| torch.zeros_like(roll), torch.zeros_like(roll), torch.ones_like(roll)], dim=1)
|
| roll_mat = roll_mat.view(roll_mat.shape[0], 3, 3)
|
|
|
| rot_mat = torch.einsum('bij,bjk,bkm->bim', pitch_mat, yaw_mat, roll_mat)
|
|
|
| return rot_mat
|
|
|
| def keypoint_transformation(kp_canonical, he, wo_exp=False):
|
| kp = kp_canonical['value']
|
| yaw, pitch, roll= he['yaw'], he['pitch'], he['roll']
|
| yaw = headpose_pred_to_degree(yaw)
|
| pitch = headpose_pred_to_degree(pitch)
|
| roll = headpose_pred_to_degree(roll)
|
|
|
| if 'yaw_in' in he:
|
| yaw = he['yaw_in']
|
| if 'pitch_in' in he:
|
| pitch = he['pitch_in']
|
| if 'roll_in' in he:
|
| roll = he['roll_in']
|
|
|
| rot_mat = get_rotation_matrix(yaw, pitch, roll)
|
|
|
| t, exp = he['t'], he['exp']
|
| if wo_exp:
|
| exp = exp*0
|
|
|
|
|
| kp_rotated = torch.einsum('bmp,bkp->bkm', rot_mat, kp)
|
|
|
|
|
| t[:, 0] = t[:, 0]*0
|
| t[:, 2] = t[:, 2]*0
|
| t = t.unsqueeze(1).repeat(1, kp.shape[1], 1)
|
| kp_t = kp_rotated + t
|
|
|
|
|
| exp = exp.view(exp.shape[0], -1, 3)
|
| kp_transformed = kp_t + exp
|
|
|
| return {'value': kp_transformed}
|
|
|
|
|
|
|
| def make_animation(source_image, source_semantics, target_semantics,
|
| generator, kp_detector, he_estimator, mapping,
|
| yaw_c_seq=None, pitch_c_seq=None, roll_c_seq=None,
|
| use_exp=True, use_half=False):
|
| with torch.no_grad():
|
| predictions = []
|
|
|
| kp_canonical = kp_detector(source_image)
|
| he_source = mapping(source_semantics)
|
| kp_source = keypoint_transformation(kp_canonical, he_source)
|
|
|
| for frame_idx in tqdm(range(target_semantics.shape[1]), 'Face Renderer:'):
|
|
|
|
|
| target_semantics_frame = target_semantics[:, frame_idx]
|
| he_driving = mapping(target_semantics_frame)
|
| if yaw_c_seq is not None:
|
| he_driving['yaw_in'] = yaw_c_seq[:, frame_idx]
|
| if pitch_c_seq is not None:
|
| he_driving['pitch_in'] = pitch_c_seq[:, frame_idx]
|
| if roll_c_seq is not None:
|
| he_driving['roll_in'] = roll_c_seq[:, frame_idx]
|
|
|
| kp_driving = keypoint_transformation(kp_canonical, he_driving)
|
|
|
| kp_norm = kp_driving
|
| out = generator(source_image, kp_source=kp_source, kp_driving=kp_norm)
|
| '''
|
| source_image_new = out['prediction'].squeeze(1)
|
| kp_canonical_new = kp_detector(source_image_new)
|
| he_source_new = he_estimator(source_image_new)
|
| kp_source_new = keypoint_transformation(kp_canonical_new, he_source_new, wo_exp=True)
|
| kp_driving_new = keypoint_transformation(kp_canonical_new, he_driving, wo_exp=True)
|
| out = generator(source_image_new, kp_source=kp_source_new, kp_driving=kp_driving_new)
|
| '''
|
| predictions.append(out['prediction'])
|
| predictions_ts = torch.stack(predictions, dim=1)
|
| return predictions_ts
|
|
|
| class AnimateModel(torch.nn.Module):
|
| """
|
| Merge all generator related updates into single model for better multi-gpu usage
|
| """
|
|
|
| def __init__(self, generator, kp_extractor, mapping):
|
| super(AnimateModel, self).__init__()
|
| self.kp_extractor = kp_extractor
|
| self.generator = generator
|
| self.mapping = mapping
|
|
|
| self.kp_extractor.eval()
|
| self.generator.eval()
|
| self.mapping.eval()
|
|
|
| def forward(self, x):
|
|
|
| source_image = x['source_image']
|
| source_semantics = x['source_semantics']
|
| target_semantics = x['target_semantics']
|
| yaw_c_seq = x['yaw_c_seq']
|
| pitch_c_seq = x['pitch_c_seq']
|
| roll_c_seq = x['roll_c_seq']
|
|
|
| predictions_video = make_animation(source_image, source_semantics, target_semantics,
|
| self.generator, self.kp_extractor,
|
| self.mapping, use_exp = True,
|
| yaw_c_seq=yaw_c_seq, pitch_c_seq=pitch_c_seq, roll_c_seq=roll_c_seq)
|
|
|
| return predictions_video |
