  In the era of deep learning, data is the critical determining factor in the
performance of neural network models. Generating large datasets suffers from
various difficulties such as scalability, cost efficiency and photorealism. To
avoid expensive and strenuous dataset collection and annotations, researchers
have inclined towards computer-generated datasets. Although, a lack of
photorealism and a limited amount of computer-aided data, has bounded the
accuracy of network predictions.
  To this end, we present WorldGen -- an open source framework to autonomously
generate countless structured and unstructured 3D photorealistic scenes such as
city view, object collection, and object fragmentation along with its rich
ground truth annotation data. WorldGen being a generative model gives the user
full access and control to features such as texture, object structure, motion,
camera and lens properties for better generalizability by diminishing the data
bias in the network. We demonstrate the effectiveness of WorldGen by presenting
an evaluation on deep optical flow. We hope such a tool can open doors for
future research in a myriad of domains related to robotics and computer vision
by reducing manual labor and the cost of acquiring rich and high-quality data.
