File size: 6,298 Bytes
a22eb82
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
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).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), 
                          torch.zeros_like(pitch), torch.cos(pitch), -torch.sin(pitch),
                          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), 
                           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):
    kp = kp_canonical['value']    # (bs, k, 3) 
    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_c' in he: 
        yaw = yaw + he['yaw_c']
    if 'pitch_c' in he: 
        pitch = pitch + he['pitch_c']
    if 'roll_c' in he: 
        roll = roll + he['roll_c'] 

    rot_mat = get_rotation_matrix(yaw, pitch, roll)    # (bs, 3, 3)

    t, exp = he['t'], he['exp']
    
    # keypoint rotation
    kp_rotated = torch.einsum('bmp,bkp->bkm', rot_mat, kp)

    # keypoint translation
    t = t.unsqueeze_(1).repeat(1, kp.shape[1], 1)
    kp_t = kp_rotated + t

    # add expression deviation 
    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, mapping, 
                            yaw_c_seq=None, pitch_c_seq=None, roll_c_seq=None,
                            use_exp=True):
    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 not use_exp:
                he_driving['exp'] = he_driving['exp']*0
            if yaw_c_seq is not None:
                he_driving['yaw_c'] = yaw_c_seq[:, frame_idx]
            if pitch_c_seq is not None:
                he_driving['pitch_c'] = pitch_c_seq[:, frame_idx]
            if roll_c_seq is not None:
                he_driving['roll_c'] = roll_c_seq[:, frame_idx]
            
            kp_driving = keypoint_transformation(kp_canonical, he_driving)
                
            #kp_norm = normalize_kp(kp_source=kp_source, kp_driving=kp_driving,
                                   #kp_driving_initial=kp_driving_initial)
            kp_norm = kp_driving
            out = generator(source_image, kp_source=kp_source, kp_driving=kp_norm)
            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