  Hand gestures are a natural means of interaction in Augmented Reality and
Virtual Reality (AR/VR) applications. Recently, there has been an increased
focus on removing the dependence of accurate hand gesture recognition on
complex sensor setup found in expensive proprietary devices such as the
Microsoft HoloLens, Daqri and Meta Glasses. Most such solutions either rely on
multi-modal sensor data or deep neural networks that can benefit greatly from
abundance of labelled data. Datasets are an integral part of any deep learning
based research. They have been the principal reason for the substantial
progress in this field, both, in terms of providing enough data for the
training of these models, and, for benchmarking competing algorithms. However,
it is becoming increasingly difficult to generate enough labelled data for
complex tasks such as hand gesture recognition. The goal of this work is to
introduce a framework capable of generating photo-realistic videos that have
labelled hand bounding box and fingertip that can help in designing, training,
and benchmarking models for hand-gesture recognition in AR/VR applications. We
demonstrate the efficacy of our framework in generating videos with diverse
backgrounds.
