  Generative models can be used to synthesize 3D objects of high quality and
diversity. However, there is typically no control over the properties of the
generated object.This paper proposes a novel generative adversarial network
(GAN) setup that generates 3D point cloud shapes conditioned on a continuous
parameter. In an exemplary application, we use this to guide the generative
process to create a 3D object with a custom-fit shape. We formulate this
generation process in a multi-task setting by using the concept of auxiliary
classifier GANs. Further, we propose to sample the generator label input for
training from a kernel density estimation (KDE) of the dataset. Our ablations
show that this leads to significant performance increase in regions with few
samples. Extensive quantitative and qualitative experiments show that we gain
explicit control over the object dimensions while maintaining good generation
quality and diversity.
