  Conditional image generation is effective for diverse tasks including
training data synthesis for learning-based computer vision. However, despite
the recent advances in generative adversarial networks (GANs), it is still a
challenging task to generate images with detailed conditioning on object
shapes. Existing methods for conditional image generation use category labels
and/or keypoints and are only give limited control over object categories. In
this work, we present SCGAN, an architecture to generate images with a desired
shape specified by an input normal map. The shape-conditioned image generation
task is achieved by explicitly modeling the image appearance via a latent
appearance vector. The network is trained using unpaired training samples of
real images and rendered normal maps. This approach enables us to generate
images of arbitrary object categories with the target shape and diverse image
appearances. We show the effectiveness of our method through both qualitative
and quantitative evaluation on training data generation tasks.
