Generative flow networks for geometric object generation.

Abstract

We employ GFlowNets for 3D geometric object generation. In order to establish empirical knowledge about the functionality and performance of the GFlowNets framework in geometric object generation tasks, which lie in continuous domains, we create a 3D point cloud environment and train GFlowNets on a benchmark 3D object dataset. The experiments show that the continuous GFlowNets model achieves similar performance compared to known methods, and discovers all modes of the target data distribution.