Fig. 3.

Overview of the proposed n-to-n multi-domain completion and segmentation framework. N = 4 and two domains (x₂, x₄) are missing in this example. Our model contains a unified content encoder E^c (red lines), domain-specific style encoders $E_i^s$ (orange lines) and generators G_i (blue lines), 1 ≤ i ≤ N. A variety of losses are adopted (burgundy lines), i.e., image consistency loss for visible domains ${\mathcal{L}}_{cyc}^x$, latent consistency loss ${\mathcal{L}}_{cyc}^c$ and ${\mathcal{L}}_{cyc}^s$, adversarial loss ${\mathcal{L}}_{adv}^x$ and reconstruction loss ${\mathcal{L}}_{rec}^x$ for the generated images. Furthermore, representational learning framework combines a segmentation generator G_S following the content code for a unified image generation and segmentation.