3D-aware Conditional Image Synthesis

We propose a 3D-aware conditional generative model for controllable photorealistic image synthesis. Given a 2D label map, such as a segmentation or edge map, our model synthesizes a photo from different viewpoints. Existing approaches fail to either synthesize images based on a conditional input or suffer from noticeable viewpoint inconsistency. Moreover, many of them lack explicit user control of 3D geometry. To tackle the aforementioned challenges, we integrate 3D representations with conditional generative modeling, i.e., enabling controllable high-resolution 3D-aware rendering by conditioning on user inputs. Our model learns to assign a semantic label to every 3D point in addition to color and density, which enables us to render the image and pixel-aligned label map simultaneously. By interactive editing of label maps projected onto user-specified viewpoints, our system can be used as a tool for 3D editing of generated content. Finally, we show that such 3D representations can be learned from widely-available monocular images and label map pairs.