  3D multi object generative models allow us to synthesize a large range of
novel 3D multi object scenes and also identify objects, shapes, layouts and
their positions. But multi object scenes are difficult to create because of the
dataset being multimodal in nature. The conventional 3D generative adversarial
models are not efficient in generating multi object scenes, they usually tend
to generate either one object or generate fuzzy results of multiple objects.
Auto-encoder models have much scope in feature extraction and representation
learning using the unsupervised paradigm in probabilistic spaces. We try to
make use of this property in our proposed model. In this paper we propose a
novel architecture using 3DConvNets trained with the progressive training
paradigm that has been able to generate realistic high resolution 3D scenes of
rooms, bedrooms, offices etc. with various pieces of furniture and objects. We
make use of the adversarial auto-encoder along with the WGAN-GP loss parameter
in our discriminator loss function. Finally this new approach to multi object
scene generation has also been able to generate more number of objects per
scene.
