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.

翻译：生成模型可用于合成质量和多样性高的三维对象。 但是,通常对生成对象的特性没有控制。 本文建议建立一个新型的基因对抗网络( GAN) 设置, 产生以连续参数为条件的三维点云形。 在一项示范应用中, 我们用这个模型来指导基因化过程, 以定制形状创建三维对象。 我们使用辅助分类器GANs 的概念在一个多任务设置中制定这一生成过程。 此外, 我们提议从数据集的内核密度估计( KDE) 中抽取用于培训的发电机标签输入。 我们的推理显示, 这导致少数样本区域的性能显著提高。 广泛的定量和定性实验表明,我们在保持优良生成质量和多样性的同时,获得了对天体层面的明确控制。