Real-time 3D cloud animations using DCGAN

Abstract: Background. Previous studies in the area of video generation using generative adversarial networks have shown limitations in perceived naturalism of the generated images. A previously proposed method of rendering and simulating clouds serves as the base for this thesis. Objectives. This thesis aims to propose a new method utilising machine learning in the context of generating 3D cloud animation in computer graphics. This aim is broken down into multiple objectives, with the primary ones being the following. The utilisation of a machine learning model includes the pre-processing of cloud images into cloud maps, training the model, as well as generating 2D cloud animations with it. 3D cloud animations are achieved by implementing the model into a pre-existing real-time cloud rendering framework. The performance of the implementation is measured and evaluated. Finally, a questionnaire is deployed and its results are analysed to evaluate the effectiveness of the proposed method. Methods. The image quality of the generated images is compared using an image quality assessment method which compares them to the data set used for training. Performance measurements are taken and compared between a base method reliant on Voronoi-noise and the proposed machine learning-based method. Finally, a questionnaire is deployed and then statistically analysed to evaluate the perceived naturalism of the base method and the proposed method. Results. The proposed method has a rendering time almost twice as long when compared to the base method when run in real-time. However, the results from the questionnaire showed that the proposed method achieves a higher level of perceived naturalism of the animation. Conclusions. The proposed method generates more natural animations than the base method at a higher cost in terms of time complexity.