Keyword [Attribute to Face]
Di X, Patel V M. Face synthesis from visual attributes via sketch using conditional vaes and gans[J]. arXiv preprint arXiv:1801.00077, 2017.
1. Overview
In this paper, it proposed Attribute2Sketch2Face
- A2S. CVAE
- S2S. GAN
- S2F. GA
1.1. Motivation
- stage-wise learning
- AUDeNet. dense UNet-based
1.2. Related Work
- Image-to-Image
- VAE
- GAN
- Autoregression
CVAE
CGAN
- CycleGAN
- Wasserstein Distance
- StackGAN
- CVAE+GAN
2. Methods
2.1. Attribute-to-Sketch (A2S)
- encoder q_Φ. encode sketch and encode attribute
- encoder q_β. encode noise and encode attribute
- only texture attribute
2.2. Sketch-to-Sketch (S2S)
- UNet. long skip to preserve low-level features
- Dense. short skip to maximize information flow
- D. patch-based
- only texture attribute
- VGG of Conv1_2
2.3. Sketch-to-Face (S2F)
- attribute consist of both color and texture
2.4. Testing
3. Experiments
3.1. Dataset
- CelebA
- LFW
CUHK
use pencil-sketch synthesis method to generate the sketch images from the face images on CelebA and LFW which lack of sketch
- select attribute of texture and color
3.2. Ablation Study
- w/o attribute in A2S
- w/o S2S
- w/o attribute concat from S2F
3.3. Metric
- Inception Score
- Attribute L2. extract attributes from MOON attribute prediction
3.4. Comparison
3.5. Synthesis
- fix noise z
- fix attrbute