2. Background Task Fashion image inpainting Some conceptions
Visual compatibility
Diversity

3. Framework Two-stage model Shape generation network.
Appearance generation network
Base on segmentation map

4. Shape generation network.
Input
An 18-channel pose heatmap
A binary mask of face and hair
An 8-channel parsing map (with a missing region)
And
A latent vector encode from VAE to encourages diversity.

5. Shape generation network.
Latent vector in shape generator
Train: Zs encoded from a binary mask of missing region by Es.
Test: Ys encoded from contextual region during testing by Ecs.
Visual compatibility relationships
To learn the correlations between the shapes of synthesized garments and contextual garments.
Enables compatibility aware sampling during inference when xs is not available— we can simply sample ys from Ecs(xc)

6. Appearance generation network
Input
A RGB image of face and hair
A RGB image (with missing region)
An 8-channel parsing map
And
A latent vector encode from VAE to encourages diversity.
Loss
Perception + Gram matrix for reconstruction
KL divergence term.