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Hand gestures are a natural means of interaction in Augmented Reality andVirtual Reality (AR/VR) applications. Recently, there has been an increasedfocus on removing the dependence of accurate hand gesture recognition oncomplex sensor setup found in expensive proprietary devices such as theMicrosoft HoloLens, Daqri and Meta Glasses. Most such solutions either rely onmulti-modal sensor data or deep neural networks that can benefit greatly fromabundance of labelled data. Datasets are an integral part of any deep learningbased research. They have been the principal reason for the substantialprogress in this field, both, in terms of providing enough data for thetraining of these models, and, for benchmarking competing algorithms. However,it is becoming increasingly difficult to generate enough labelled data forcomplex tasks such as hand gesture recognition. The goal of this work is tointroduce a framework capable of generating photo-realistic videos that havelabelled hand bounding box and fingertip that can help in designing, training,and benchmarking models for hand-gesture recognition in AR/VR applications. Wedemonstrate the efficacy of our framework in generating videos with diversebackgrounds.

Synthetic Video Generation for Robust Hand Gesture Recognition in Augmented Reality Applications