Supplemental Materials
DeepFaceDrawing: Deep Generation of Face Images fromSketches
SHU-YU CHEN∗ , Institute of Computing Technology, CAS and UCAS
WANCHAO SU∗ , School of Creative Media, City University of Hong Kong
LIN GAO† , Institute of Computing Technology, CAS and and UCASSHIHONG XIA, Institute of Computing Technology, CAS and and UCAS
HONGBO FU, School of Creative Media, City University of Hong Kong
1 OVERVIEW
In this supplemental material, we have listed the conducted experiments, additional gallery of theuser study results, and more details of the implementation.
2 ABLATION STUDY OF K SELECTION

Input SketchK =3K =6K = 10K = 15

K = 20Fig. 1. The effect of selection of K in manifold projection. We set K = 10 in our implementation.

3 GALLERY OF THE USER STUDY RESULTS
We have listed more results obtained from the usability study in Figures 2 and 3.
4 SKETCHES USED IN THE PERCEPTIVE EVALUATION STUDY
In this section, we demonstrate the input sketches used in the perceptive evaluation in Figure 4.
5 COMPARISON OF DIFFERENT METHODS WITH TEST SET INPUT
We illustrate the visual comparison given the test set sketches (i.e., the edge maps) as input inFigure 5.
6 ABLATION STUDY OF THE SELECTION OF THE FEATURE DIMENSION
We show the MSE metrics of the reconstructed sketch with different latent feature dimensions.
The quantitative results are elaborated in Table 1.
∗ Authors contributed equally.
† Corresponding author.

Authors’ addresses: Shu-Yu Chen, Lin Gao and Shihong Xia are with the Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology, Chinese Academy of Sciences. Wanchao Su and Hongbo
Fu are with the School of Creative Media, City University of Hong Kong. Authors’ e-mails: chenshuyu@ict.ac.cn, wanchao.su@my.cityu.edu.hk, gaolin@ict.ac.cn, xsh@ict.ac.cn, hongbofu@cityu.edu.hk.

2Shu-Yu Chen, Wanchao Su, Lin Gao, Shihong Xia, and Hongbo Fu

Fig. 2. More results: Part I.

Latent Dim
128
256
512

left-eye right-eye nose
0.370
0.323
0.423
0.247
0.228
0.288
0.131
0.126
0.177

mouth
0.359
0.287
0.197

Table 1. An ablation study for the number of feature dimensions in Component Embendding. Comparing
the value of the MSE loss for each component in different feature dimensions, the loss decreases with theincrease of the number of feature dimensions.

7 NETWORK ARCHITECTURES
Our network consists of Component Embedding, Feature Mapping Module and Image Synthesis
Module. In training, we use Adam optimizer [Kingma and Ba 2014] with β1 = 0.5 and β2 = 0.999
and the initial learning rate is 0.0002.
7.1

Component Embedding Architectures

Our Component Embedding module contains five auto-encoders. Each auto-encoder consists offive encoding layers (Table 2) and five decoding layers (Table 3). The encoding layer is shown inTable 2. We add a fully connected layer in the middle to ensure the latent descriptor is of 512

Supplemental Materials
DeepFaceDrawing: Deep Generation of Face Images from Sketches

3Fig. 3. More results: Part II.

dimensions for all the five components. The details of Component Embedding module are shownin Table 4.
7.2

Feature Matching Architectures

The Feature Matching module contains five decoding networks, which take as input the compact
feature vectors obtained from the component manifolds and convert them to the corresponding
size of feature maps for subsequent generation. See the details in Table 5.

4

Shu-Yu Chen, Wanchao Su, Lin Gao, Shihong Xia, and Hongbo Fu

Fig. 4. Sketch inputs used in the perceptive evaluation.

Fig. 5. Generation results given test set sketches. For columns from left to right: pix2pix, Lines2FacePhoto,pix2pixHD, iSketchNFill and Ours.

Supplemental Materials
DeepFaceDrawing: Deep Generation of Face Images from Sketches

7.3

5Image Synthesis Architectures

Our Image Synthesis module adopts a GAN architecture utilizing a generator and a discriminatorto generate real face images from the fused feature maps. The details of the generator are illustrated in Table 6. The discriminator employs a multi-scale discriminating manner: scale the input
feature maps and the generated images in three different levels and go through three different
sub-discriminators. See Table 7 for more details.
F(x)Conv2dBatchNorm2d
LeakyReLU

F(x)ConvTranspose2d
BatchNorm2d
LeakyReLU

Table 2. Conv2D-Block

Table 3. ConvTrans2D-Block

LayerInput
Conv2D-BlockResnet-Block
Conv2D-BlockResnet-Block
Conv2D-BlockResnet-Block
Conv2D-BlockResnet-Block
Conv2D-BlockResnet-Block
Fully Connected

Output Size
1 × H ×W
32 × H2 × W232 × H2 × W264 × H4 × W4
64 × H4 × W4
128 × H8 × W8
128 × H8 × W8
H
256 × 16×W
16H
256 × 16 × W
16
H×W
512 × 32
32
H
512 × 32×W
32512

Filter
1 → 3232 → 32 → 32
32 → 64
64 → 64 → 64
64 → 128
128 → 128 → 128
128 → 256
256 → 256 → 256
256 → 512
512 → 512 → 512H
512 × 32×W
32 → 512

Fully Connected
Resnet-Block
ConvTrans2D-BlockResnet-BlockConvTrans2D-BlockResnet-Block
ConvTrans2D-BlockResnet-Block
ConvTrans2D-BlockResnet-Block
ConvTrans2D-BlockResnet-Block
ReflectionPad2d
Conv2D-Block
OutputH
512 × 32×W32H
512 × 32 × W
32H
256 × 16×W
16H
256 × 16×W
16
128 × H8 × W8
128 × H8 × W8
64 × H4 × W4
64 × H4 × W4
32 × H2 × W2
32 × H2 × W2
32 × H × W
32 × H × W

H
512 → 512 × 32
×W32
512 → 512 → 512
512 → 256
256 → 256 → 256
256 → 128
128 → 128 → 128
128 → 64
64 → 64 → 64
64 → 32
32 → 32 → 3232 → 32
32 → 32 → 32

1 × H ×W
1 × H ×W

32 → 1Table 4. The architecture of the Component Embedding Module.

6Shu-Yu Chen, Wanchao Su, Lin Gao, Shihong Xia, and Hongbo Fu

Layer
Input
Fully Connected
Resnet-Block
ConvTrans2D-BlockResnet-Block
ConvTrans2D-BlockResnet-Block
ConvTrans2D-BlockResnet-Block
ConvTrans2D-BlockResnet-Block
ConvTrans2D-BlockResnet-Block
ReflectionPad2d
Conv2D-Block
OutputOutput Size
512H
512 × 32×W
32H
512 × 32×W
32H
256 × 16×W
16H
256 × 16×W
16
256 × H8 × W8
256 × H8 × W8128 × H4 × W4
128 × H4 × W4
64 × H2 × W2
64 × H2 × W2
64 × H × W
64 × H × W
32 × H × W
32 × H × W

FilterH
512 → 512 × 32
×W32
512 → 512 → 512
512 → 256
256 → 256 → 256
256 → 256
256 → 256 → 256
256 → 128
128 → 128 → 128
128 → 64
64 → 64 → 64
64 → 64
64 → 64 → 64

64 → 32

Table 5. The architecture of the Feature Matching Module.

Layer
Input
Conv2D-Block
Conv2D-Block
Conv2D-Block
Conv2D-BlockResnet-Block (×9)
ConvTrans2D-Block
ConvTrans2D-Block
ConvTrans2D-BlockConvTrans2D-Block
OutputGenerator
Output Size
Filter32 × 512 × 512
56 × 512 × 512
32 → 56
112 × 256 × 256
56 → 112224 × 128 × 128
112 → 224
448 × 64 × 64
224 → 448
448 × 64 × 64 448 → 448 → 448
224 × 128 × 128
448 → 224
112 × 256 × 256
224 → 112224 × 512 × 512
448 → 224
3 × 512 × 512
224 → 3
3 × 512 × 512

Table 6. The architecture of Generator in the Image Synthesis Module.

Supplemental Materials
DeepFaceDrawing: Deep Generation of Face Images from Sketches

Discriminating Unit (DisUnit)
Layer
Output Size
FilterInput(32 + 3) × H × W
Conv2D-Block
64 × H2 × W2
(32 + 3) → 64
Conv2D-Block
128 × H4 × W4
64 → 128
Conv2D-Block
256 × H8 × W8
128 → 256
Conv2D-Block
512 × H8 × W8
256 → 512
Conv2D-Block
512 × H8 × W8
512 → 512
Output
512 × H8 × W8
Discriminator
Layer
D1 Output Size D2 Output Size D3 Output Size
Input
35 × 512 × 512
35 × 512 × 512
35 × 512 × 512
AvgPool
35 × 256 × 256
35 × 256 × 256
AvgPool
35 × 128 × 128
DisUnit
512 × 64 × 64
512 × 32 × 32512 × 32 × 32
Output
512 × 64 × 64
512 × 32 × 32
512 × 32 × 32
Table 7. The architecture of the Discriminator in Image Synthesis Module.

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